US20200302548A1 - Accident fault determination for autonomous vehicles - Google Patents

Accident fault determination for autonomous vehicles Download PDF

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Publication number
US20200302548A1
US20200302548A1 US16/895,408 US202016895408A US2020302548A1 US 20200302548 A1 US20200302548 A1 US 20200302548A1 US 202016895408 A US202016895408 A US 202016895408A US 2020302548 A1 US2020302548 A1 US 2020302548A1
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United States
Prior art keywords
vehicle
autonomous
autonomous operation
fault
operation features
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Application number
US16/895,408
Inventor
Blake Konrardy
Scott T. Christensen
Gregory Hayward
Scott Farris
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State Farm Mutual Automobile Insurance Co
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State Farm Mutual Automobile Insurance Co
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Publication date
Application filed by State Farm Mutual Automobile Insurance Co filed Critical State Farm Mutual Automobile Insurance Co
Priority to US16/895,408 priority Critical patent/US20200302548A1/en
Assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY reassignment STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CHRISTENSEN, SCOTT T., HAYWARD, GREGORY, FARRIS, SCOTT, KONRARDY, BLAKE
Publication of US20200302548A1 publication Critical patent/US20200302548A1/en
Pending legal-status Critical Current

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    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88Radar or analogous systems specially adapted for specific applications
    • G01S13/93Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G01S2013/9318Controlling the steering
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/10Geometric CAD
    • G06F30/15Vehicle, aircraft or watercraft design
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • G06Q50/40
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00Registering or indicating the working of vehicles
    • G07C5/08Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0841Registering performance data
    • G07C5/085Registering performance data using electronic data carriers
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30Services specially adapted for particular environments, situations or purposes
    • H04W4/40Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/46Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]

Definitions

  • the present disclosure generally relates to systems and methods for determining risk, pricing, and offering vehicle insurance policies, specifically vehicle insurance policies where vehicle operation is partially or fully automated.
  • Vehicle or automobile insurance exists to provide financial protection against physical damage and/or bodily injury resulting from traffic accidents and against liability that could arise therefrom.
  • a customer purchases a vehicle insurance policy for a policy rate having a specified term.
  • the insurer pays for damages to the insured which are caused by covered perils, acts, or events as specified by the language of the insurance policy.
  • the payments from the insured are generally referred to as “premiums,” and typically are paid on behalf of the insured over time at periodic intervals.
  • An insurance policy may remain “in-force” while premium payments are made during the term or length of coverage of the policy as indicated in the policy.
  • An insurance policy may “lapse” (or have a status or state of “lapsed”), for example, when premium payments are not being paid or if the insured or the insurer cancels the policy.
  • Premiums may be typically determined based upon a selected level of insurance coverage, location of vehicle operation, vehicle model, and characteristics or demographics of the vehicle operator.
  • the characteristics of a vehicle operator that affect premiums may include age, years operating vehicles of the same class, prior incidents involving vehicle operation, and losses reported by the vehicle operator to the insurer or a previous insurer.
  • Past and current premium determination methods do not, however, account for use of autonomous vehicle operating features.
  • the present embodiments may, inter alia, alleviate this and/or other drawbacks associated with conventional techniques.
  • the present embodiments may be related to autonomous or semi-autonomous vehicle functionality, including driverless operation, accident avoidance, or collision warning systems. These autonomous vehicle operation features may either assist the vehicle operator to more safely or efficiently operate a vehicle or may take full control of vehicle operation under some or all circumstances.
  • the present embodiments may also facilitate risk assessment and premium determination for vehicle insurance policies covering vehicles with autonomous operation features.
  • the disclosure herein generally addresses systems and methods for determining risk levels associated with one or more autonomous (and/or semi-autonomous) operation features for controlling a vehicle or assisting a vehicle operator in controlling the vehicle.
  • a server or other computer system may present test input signals to the one or more autonomous operation features to test the response of the features in a virtual environment. This virtual testing may include presentation of fixed inputs or may include a simulation of a dynamic virtual environment in which a virtual vehicle is controlled by the one or more autonomous operation features.
  • the one or more autonomous operation features generate output signals that may then be used to determine the effectiveness of the control decisions by predicting the responses of vehicles to the output signals.
  • Risk levels associated with the effectiveness of the autonomous operation features may be used to determine a premium for an insurance policy associated with the vehicle, which may be determined by reference to a risk category.
  • a computer-implemented method for testing the effectiveness of one or more autonomous operation features for controlling a virtual vehicle in a virtual test environment may be provided.
  • the computer-implemented method may include receiving a set of computer-readable instructions for implementing the one or more autonomous operation features, executing the one or more software routines, receiving one or more test input signals that simulate the one or more signals from at least one sensor, generating one or more test output signals for the virtual vehicle in response to the received one or more test input signals, predicting one or more responses of the virtual vehicle in the virtual test environment to the one or more test output signals, and/or determining a measure of the effectiveness of the one or more autonomous operation features based upon the one or more predicted responses of the virtual vehicle to the one or more test output signals.
  • the set of computer-readable instructions may include one or more software routines configured to receive one or more input signals from at least one sensor and generate one or more output signals for controlling a vehicle.
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer system for testing the effectiveness of one or more autonomous operation features for controlling a virtual vehicle in a virtual test environment may include one or more processors and a non-transitory program memory coupled to the one or more processors and storing executable instructions.
  • the executable instruction may, when executed by the one or more processors, cause the computer system to receive a set of computer-readable instructions for implementing the one or more autonomous operation features, execute the one or more software routines, receive one or more test input signals that simulate the one or more signals from at least one sensor, generate one or more test output signals for the virtual vehicle in response to the received one or more test input signals, predict one or more responses of the virtual vehicle in the virtual test environment to the one or more test output signals, and/or determine a measure of the effectiveness of the one or more autonomous operation features based upon the one or more predicted responses of the virtual vehicle to the one or more test output signals.
  • the set of computer-readable instructions may include one or more software routines configured to receive one or more input signals from at least one sensor and generate one or more output signals for controlling a vehicle.
  • the system may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a tangible, non-transitory computer-readable medium storing instructions for testing the effectiveness of one or more autonomous operation features for controlling a virtual vehicle in a virtual test environment.
  • the instructions may, when executed by at least one processor of a computer system, cause the computer system to receive a set of computer-readable instructions for implementing the one or more autonomous operation features, execute the one or more software routines, receive one or more test input signals that simulate the one or more signals from at least one sensor, generate one or more test output signals for the virtual vehicle in response to the received one or more test input signals, predict one or more responses of the virtual vehicle in the virtual test environment to the one or more test output signals, and/or determine a measure of the effectiveness of the one or more autonomous operation features based upon the one or more predicted responses of the virtual vehicle to the one or more test output signals.
  • the set of computer-readable instructions may include one or more software routines configured to receive one or more input signals from at least one sensor and generate one or more output signals for controlling a vehicle.
  • the instructions of the computer-readable medium may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • the one or more test input signals may be received from a database containing a plurality of test signals.
  • the test input signals may be received by generating a simulation of the virtual vehicle in the virtual test environment, determining simulated sensor data associated with the virtual vehicle in the virtual test environment, and determining the one or more test input signals based upon the simulated sensor data.
  • Determining the one or more risk levels associated with the one or more autonomous operation features may include predicting the one or more risk levels based upon a comparison of (i) the one or more test output signals generated by the one or more software routines, (ii) one or more other test output signals generated by one or more other software routines of one or more other autonomous operation features in response to one or more other test input signals, and/or (iii) observed operating data regarding the one or more other autonomous operation features disposed within a plurality of other vehicles operating outside the virtual test environment. Additionally, the observed operating data may include data regarding actual losses associated with insurance policies covering the plurality of other vehicles having the one or more other autonomous operation features.
  • the disclosure herein also generally addresses systems and methods for monitoring the use of a vehicle having one or more autonomous (and/or semi-autonomous) operation features and determining risk associated with the one or more autonomous (and/or semi-autonomous) operation features based upon control decisions generated by the one or more autonomous (and/or semi-autonomous) operation features.
  • An on-board computer or mobile device may monitor and record vehicle operating data, including information regarding the decisions made by the autonomous operation features, regardless of whether the decisions are actually used to control the vehicle.
  • a server may receive the operating data and may process this data to determine risk levels associated with operation of the vehicle under the current conditions using a variety of available autonomous operation features, configurations, or settings.
  • a computer-implemented method for monitoring a vehicle having one or more autonomous operation features for controlling the vehicle may be provided.
  • the computer-implemented method may include receiving operating data regarding operation of the vehicle, recording a log of the received operating data, receiving actual loss data regarding losses associated with insurance policies covering a plurality of other vehicles having the one or more autonomous operation features, and/or determining at least one risk level associated with the vehicle based at least in part upon the recorded log of the operating data and the received actual loss data.
  • the operating data may include (i) information from one or more sensors disposed within the vehicle, (ii) information regarding the one or more autonomous operation features, and/or (iii) information regarding control decisions generated by the one or more autonomous operation features.
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer system for monitoring a vehicle having one or more autonomous operation features for controlling the vehicle may include one or more processors and a non-transitory program memory coupled to the one or more processors and storing executable instructions.
  • the executable instruction may, when executed by the one or more processors, cause the computer system to receive operating data regarding operation of the vehicle, record a log of the received operating data, receive actual loss data regarding losses associated with insurance policies covering a plurality of other vehicles having the one or more autonomous operation features, and/or determine at least one risk level associated with the vehicle based at least in part upon the recorded log of the operating data and the received actual loss data.
  • the operating data may include (i) information from one or more sensors disposed within the vehicle, (ii) information regarding the one or more autonomous operation features, and/or (iii) information regarding control decisions generated by the one or more autonomous operation features.
  • the system may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a tangible, non-transitory computer-readable medium storing instructions for monitoring a vehicle having one or more autonomous operation features for controlling the vehicle.
  • the instructions may, when executed by at least one processor of a computer system, cause the computer system to receive operating data regarding operation of the vehicle, record a log of the received operating data, receive actual loss data regarding losses associated with insurance policies covering a plurality of other vehicles having the one or more autonomous operation features, and/or determine at least one risk level associated with the vehicle based at least in part upon the recorded log of the operating data and the received actual loss data.
  • the operating data may include (i) information from one or more sensors disposed within the vehicle, (ii) information regarding the one or more autonomous operation features, and/or (iii) information regarding control decisions generated by the one or more autonomous operation features.
  • the instructions of the computer-readable medium may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • the information regarding the control decisions generated by the one or more autonomous operation features may include information regarding control decisions not implemented to control the vehicle, which may include the following: an alternative control decision not selected by the one or more autonomous operation features to control the vehicle and/or a control decision not implemented because the autonomous operation feature was disabled.
  • Each entry in the log of the operating data may include a timestamp associated with the recorded operating data, and each timestamp may include the following: date, time, location, vehicle environment, vehicle condition, autonomous operation feature settings, and/or autonomous operation feature configuration information.
  • External data regarding the vehicle environment for each entry in the log of the operating data may be further included, including information regarding the following: road conditions, weather conditions, nearby traffic conditions, type of road, construction conditions, presence of pedestrians, presence of other obstacles, and/or availability of autonomous communications from external sources.
  • the external data may be associated with log entries based upon the timestamp associated with each entry.
  • the at least one risk level associated with the vehicle may be further determined based at least in part upon the external data regarding the vehicle environment.
  • the operating data may be received by a mobile device within the vehicle. The mobile device may communicate the received operating data to a server via a network, and the server may record the log of the operating data.
  • Some systems or methods may further receive a request for a quote of a premium associated with a vehicle insurance policy and presenting an option to purchase the vehicle insurance policy to a customer associated with the vehicle. They may also determine a premium associated with the vehicle insurance policy based upon the at least one risk level.
  • the disclosure herein also generally addresses systems and methods for monitoring the use of a vehicle having one or more autonomous (and/or semi-autonomous) operation features and determining fault following the occurrence of an accident involving the vehicle.
  • An on-board computer or mobile device may monitor and record vehicle operating data, including sensor data and data from the one or more autonomous operation features.
  • a server may receive the operating data and may process this data to determine the cause of and fault for the accident. These fault determination may then be used, at least in part (i.e., wholly or partially), to determine coverage levels associated with an insurance policy associated with the vehicle and/or an adjustment to risk levels associated with the autonomous operation features.
  • a computer-implemented method for determining fault relating to a collision or other loss may be provided.
  • the computer-implemented method may include receiving an indication of an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features for controlling the vehicle, receiving operating data regarding operation of the vehicle during a time period including the time of the accident, receiving information regarding use levels of the one or more autonomous operation features during the time period including the time of the accident, determining an allocation of fault for the accident based upon, at least in part (i.e., wholly or partially), the received operating data and the use levels of the one or more autonomous operation features, and/or determining one or more coverage levels associated with a vehicle insurance policy based upon, at least in part (i.e., wholly or partially), the determined allocation of fault.
  • the indication of the accident may be generated based upon the received operating data.
  • the operating data may include information from one or more sensors disposed within the vehicle and/or information regarding the operation of the one or more autonomous operation features.
  • the autonomous operation features may include one or more autonomous communication features, in which case the operating data may include communication data from external sources.
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer system for determining fault relating to a collision or other loss may be provided.
  • the computer system may include one or more processors, one or more communication modules adapted to communicate data, and a non-transitory program memory coupled to the one or more processors and storing executable instructions.
  • the executable instruction may, when executed by the one or more processors, cause the computer system to receive an indication of an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features for controlling the vehicle, receive operating data regarding operation of the vehicle during a time period including the time of the accident, receive information regarding use levels of the one or more autonomous operation features during the time period including the time of the accident, determine an allocation of fault for the accident based upon the received operating data and the use levels of the one or more autonomous operation features, and/or determine one or more coverage levels associated with a vehicle insurance policy based upon the determined allocation of fault.
  • the indication of the accident may be generated based upon the received operating data.
  • the operating data may include information from one or more sensors disposed within the vehicle and/or information regarding the operation of the one or more autonomous operation features. Additionally, the autonomous operation features may include one or more autonomous communication features, in which case the operating data may include communication data from external sources.
  • the system may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a tangible, non-transitory computer-readable medium storing instructions for determining fault relating to a collision or other loss.
  • the instructions may, when executed by at least one processor of a computer system, cause the computer system to receive an indication of an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features for controlling the vehicle, receive operating data regarding operation of the vehicle during a time period including the time of the accident, receive information regarding use levels of the one or more autonomous operation features during the time period including the time of the accident, determine an allocation of fault for the accident based upon the received operating data and the use levels of the one or more autonomous operation features, and/or determine one or more coverage levels associated with a vehicle insurance policy based upon the determined allocation of fault.
  • the indication of the accident may be generated based upon the received operating data.
  • the operating data may include information from one or more sensors disposed within the vehicle and/or information regarding the operation of the one or more autonomous operation features.
  • the autonomous operation features may include one or more autonomous communication features, in which case the operating data may include communication data from external sources.
  • the instructions of the computer-readable medium may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • Determining the allocation of fault may include allocating fault for the accident between one or more of the following: the vehicle operator, the one or more autonomous operation (and/or semi-autonomous) features as a group, each of the one or more autonomous (and/or semi-autonomous) operation features separately, and/or a third party.
  • Allocating fault may further include determining one or more of the following: a point of impact on the vehicle, a point of impact on one or more additional vehicles, a velocity of the vehicle, a velocity of one or more additional vehicles, a movement of the vehicle, a movement of one or more additional vehicles, a location of one or more obstructions, a movement of one or more obstructions, a location of one or more pedestrians, a movement of one or more pedestrians, a measure of road surface integrity, a measure of road surface friction, a location of one or more traffic signs, a location of one or more traffic signals, an indication of a state of one or more traffic signals, a control signal generated by one or more autonomous operation features of the vehicle, and/or a control signal generated by one or more autonomous operation features of one or more additional vehicles.
  • the one or more coverage levels may be determined based upon whether the determined allocation of fault indicates a vehicle operator is at least partially at fault for the accident.
  • the coverage levels may be further determined based upon the proportion of fault allocated to the vehicle operator.
  • the one or more coverage levels may include the following: a deductible, a type of coverage, a maximum coverage limit, an estimate of a cost to repair the vehicle, an estimate of a cost to replace the vehicle, an estimate of a cost to repair other property, an estimate of a cost to replace other property, and/or an estimate of a payment of medical expenses.
  • determining the one or more coverage levels may further include determining to cancel the vehicle insurance policy.
  • Some embodiments may include determining an adjustment to a cost associated with a vehicle insurance policy based upon the allocation of fault when the at least a portion of the fault is determined to be allocated to the vehicle operator. Additionally, some embodiments may include determining an adjustment to a risk level associated with one or more autonomous (and/or semi-autonomous) operation features based upon the allocation of fault when the at least a portion of the fault is determined to be allocated to the one or more autonomous (and/or semi-autonomous) operation features.
  • the cost associated with the vehicle insurance policy may include a premium, a surcharge, a penalty, a rate, and/or a rate category.
  • Other embodiments may include presenting the determined allocation of fault and/or the determined coverage levels to a reviewer for verification and/or receiving an indication of verification from the reviewer.
  • the system or method may cause one or more options associated with the determined one or more coverage levels to be presented to a customer for review.
  • a selection by the customer of at least one of the one or more options may be received, based upon which the vehicle insurance policy may be adjusted.
  • FIG. 1 illustrates a block diagram of an exemplary computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes in accordance with the described embodiments;
  • FIG. 2 illustrates a block diagram of an exemplary on-board computer or mobile device
  • FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicle operation method in accordance with the presently described embodiments
  • FIGS. 4A-B illustrate flow diagrams of an exemplary autonomous vehicle operation monitoring method in accordance with the presently described embodiments
  • FIG. 5 illustrates a flow diagram of an exemplary autonomous operation feature evaluation method for determining the effectiveness of autonomous operation features in accordance with the presently described embodiments
  • FIG. 6 illustrates a flow diagram of an exemplary autonomous operation feature testing method for presenting test conditions to an autonomous operation feature and observing and recording responses to the test conditions in accordance with the presently described embodiments;
  • FIG. 7 illustrates a flow diagram of an exemplary autonomous feature evaluation method for determining the effectiveness of an autonomous operation feature under a set of environmental conditions, configuration conditions, and settings in accordance with the presently described embodiments;
  • FIG. 8 illustrates a flow diagram depicting an exemplary embodiment of a fully autonomous vehicle insurance pricing method in accordance with the presently described embodiments
  • FIG. 9 illustrates a flow diagram depicting an exemplary embodiment of a partially autonomous vehicle insurance pricing method in accordance with the presently described embodiments.
  • FIG. 10 illustrates a flow diagram depicting an exemplary embodiment of an autonomous vehicle insurance pricing method for determining risk and premiums for vehicle insurance policies covering autonomous vehicles with autonomous communication features in accordance with the presently described embodiments.
  • FIG. 11 illustrates a flow diagram of an exemplary autonomous operation feature monitoring and feedback method
  • FIG. 12 illustrates a flow diagram of an exemplary autonomous operation feature monitoring and alert method
  • FIG. 13 illustrates a flow diagram of an exemplary fault determination method for determining fault following an accident based upon sensor data and communication data
  • FIG. 14 illustrates a high-level flow diagram of an exemplary autonomous automobile insurance pricing system.
  • the systems and methods disclosed herein generally relate to evaluating, monitoring, pricing, and processing vehicle insurance policies for vehicles including autonomous (or semi-autonomous) vehicle operation features.
  • the autonomous operation features may take full control of the vehicle under certain conditions, viz. fully autonomous operation, or the autonomous operation features may assist the vehicle operator in operating the vehicle, viz. partially autonomous operation.
  • Fully autonomous operation features may include systems within the vehicle that pilot the vehicle to a destination with or without a vehicle operator present (e.g., an operating system for a driverless car).
  • Partially autonomous operation features may assist the vehicle operator in limited ways (e.g., automatic braking or collision avoidance systems).
  • the autonomous operation features may affect the risk related to operating a vehicle, both individually and/or in combination.
  • some embodiments evaluate the quality of each autonomous operation feature and/or combination of features. This may be accomplished by testing the features and combinations in controlled environments, as well as analyzing the effectiveness of the features in the ordinary course of vehicle operation. New autonomous operation features may be evaluated based upon controlled testing and/or estimating ordinary-course performance based upon data regarding other similar features for which ordinary-course performance is known.
  • Some autonomous operation features may be adapted for use under particular conditions, such as city driving or highway driving. Additionally, the vehicle operator may be able to configure settings relating to the features or may enable or disable the features at will. Therefore, some embodiments monitor use of the autonomous operation features, which may include the settings or levels of feature use during vehicle operation. Information obtained by monitoring feature usage may be used to determine risk levels associated with vehicle operation, either generally or in relation to a vehicle operator. In such situations, total risk may be determined by a weighted combination of the risk levels associated with operation while autonomous operation features are enabled (with relevant settings) and the risk levels associated with operation while autonomous operation features are disabled. For fully autonomous vehicles, settings or configurations relating to vehicle operation may be monitored and used in determining vehicle operating risk.
  • Risk category or price may be determined based upon factors relating to the evaluated effectiveness of the autonomous vehicle features.
  • the risk or price determination may also include traditional factors, such as location, vehicle type, and level of vehicle use. For fully autonomous vehicles, factors relating to vehicle operators may be excluded entirely. For partially autonomous vehicles, factors relating to vehicle operators may be reduced in proportion to the evaluated effectiveness and monitored usage levels of the autonomous operation features.
  • the risk level and/or price determination may also include an assessment of the availability of external sources of information. Location and/or timing of vehicle use may thus be monitored and/or weighted to determine the risk associated with operation of the vehicle.
  • the present embodiments may relate to assessing and pricing insurance based upon autonomous (or semi-autonomous) functionality of a vehicle, and not the human driver.
  • a smart vehicle may maneuver itself without human intervention and/or include sensors, processors, computer instructions, and/or other components that may perform or direct certain actions conventionally performed by a human driver.
  • An analysis of how artificial intelligence facilitates avoiding accidents and/or mitigates the severity of accidents may be used to build a database and/or model of risk assessment. After which, automobile insurance risk and/or premiums (as well as insurance discounts, rewards, and/or points) may be adjusted based upon autonomous or semi-autonomous vehicle functionality, such as by groups of autonomous or semi-autonomous functionality or individual features.
  • an evaluation may be performed of how artificial intelligence, and the usage thereof, impacts automobile accidents and/or automobile insurance claims.
  • the types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road
  • the adjustments to automobile insurance rates or premiums based upon the autonomous or semi-autonomous vehicle-related functionality or technology may take into account the impact of such functionality or technology on the likelihood of a vehicle accident or collision occurring.
  • a processor may analyze historical accident information and/or test data involving vehicles having autonomous or semi-autonomous functionality. Factors that may be analyzed and/or accounted for that are related to insurance risk, accident information, or test data may include (1) point of impact; (2) type of road; (3) time of day; (4) weather conditions; (5) road construction; (6) type/length of trip; (7) vehicle style; (8) level of pedestrian traffic; (9) level of vehicle congestion; (10) atypical situations (such as manual traffic signaling); (11) availability of internet connection for the vehicle; and/or other factors. These types of factors may also be weighted according to historical accident information, predicted accidents, vehicle trends, test data, and/or other considerations.
  • the benefit of one or more autonomous or semi-autonomous functionalities or capabilities may be determined, weighted, and/or otherwise characterized.
  • the benefit of certain autonomous or semi-autonomous functionality may be substantially greater in city or congested traffic, as compared to open road or country driving traffic.
  • certain autonomous or semi-autonomous functionality may only work effectively below a certain speed, i.e., during city driving or driving in congestion.
  • Other autonomous or semi-autonomous functionality may operate more effectively on the highway and away from city traffic, such as cruise control.
  • Further individual autonomous or semi-autonomous functionality may be impacted by weather, such as rain or snow, and/or time of day (day light versus night).
  • fully automatic or semi-automatic lane detection warnings may be impacted by rain, snow, ice, and/or the amount of sunlight (all of which may impact the imaging or visibility of lane markings painted onto a road surface, and/or road markers or street signs).
  • Automobile insurance premiums, rates, discounts, rewards, refunds, points, etc. may be adjusted based upon the percentage of time or vehicle usage that the vehicle is the driver, i.e., the amount of time a specific driver uses each type of autonomous (or even semi-autonomous) vehicle functionality.
  • insurance premiums, discounts, rewards, etc. may be adjusted based upon the percentage of vehicle usage during which the autonomous or semi-autonomous functionality is in use.
  • automobile insurance risk, premiums, discounts, etc. for an automobile having one or more autonomous or semi-autonomous functionalities may be adjusted and/or set based upon the percentage of vehicle usage that the one or more individual autonomous or semi-autonomous vehicle functionalities are in use, anticipated to be used or employed by the driver, and/or otherwise operating.
  • Such usage information for a particular vehicle may be gathered over time and/or via remote wireless communication with the vehicle.
  • One embodiment may involve a processor on the vehicle, such as within a vehicle control system or dashboard, monitoring in real-time whether vehicle autonomous or semi-autonomous functionality is currently operating.
  • Other types of monitoring may be remotely performed, such as via wireless communication between the vehicle and a remote server, or wireless communication between a vehicle-mounted dedicated device (that is configured to gather autonomous or semi-autonomous functionality usage information) and a remote server.
  • the vehicle may send a Vehicle-to-Vehicle (V2V) wireless communication to a nearby vehicle also employing the same or other type(s) of autonomous or semi-autonomous functionality.
  • V2V Vehicle-to-Vehicle
  • the V2V wireless communication from the first vehicle to the second vehicle may indicate that the first vehicle is autonomously braking, and the degree to which the vehicle is automatically braking and/or slowing down.
  • the second vehicle may also automatically or autonomously brake as well, and the degree of automatically braking or slowing down of the second vehicle may be determined to match, or even exceed, that of the first vehicle.
  • the second vehicle traveling directly or indirectly, behind the first vehicle, may autonomously safely break in response to the first vehicle autonomously breaking.
  • the V2V wireless communication from the first vehicle to the second vehicle may indicate that the first vehicle is beginning or about to change lanes or turn.
  • the second vehicle may autonomously take appropriate action, such as automatically slow down, change lanes, turn, maneuver, etc. to avoid the first vehicle.
  • the present embodiments may include remotely monitoring, in real-time and/or via wireless communication, vehicle autonomous or semi-autonomous functionality. From such remote monitoring, the present embodiments may remotely determine that a vehicle accident has occurred. As a result, emergency responders may be informed of the location of the vehicle accident, such as via wireless communication, and/or quickly dispatched to the accident scene.
  • the present embodiments may also include remotely monitoring, in real-time or via wireless communication, that vehicle autonomous or semi-autonomous functionality is, or is not, in use, and/or collect information regarding the amount of usage of the autonomous or semi-autonomous functionality. From such remote monitoring, a remote server may remotely send a wireless communication to the vehicle to prompt the human driver to engage one or more specific vehicle autonomous or semi-autonomous functionalities.
  • a traffic light may wirelessly indicate to the vehicle that the traffic light is about to switch from green to yellow, or from yellow to red.
  • the autonomous or semi-autonomous vehicle may automatically start to brake, and/or present or issue a warning/alert to the human driver.
  • the vehicle may wirelessly communicate with the vehicles traveling behind it that the traffic light is about to change and/or that the vehicle has started to brake or slow down such that the following vehicles may also automatically brake or slow down accordingly.
  • Insurance premiums, rates, ratings, discounts, rewards, special offers, points, programs, refunds, claims, claim amounts, etc. may be adjusted for, or may otherwise take into account, the foregoing functionality and/or the other functionality described herein.
  • insurance policies may be updated based upon autonomous or semi-autonomous vehicle functionality; V2V wireless communication-based autonomous or semi-autonomous vehicle functionality; and/or vehicle-to-infrastructure or infrastructure-to-vehicle wireless communication-based autonomous or semi-autonomous vehicle functionality.
  • Insurance providers may currently develop a set of rating factors based upon the make, model, and model year of a vehicle. Models with better loss experience receive lower factors, and thus lower rates.
  • This current rating system cannot be used to assess risk for autonomous technology is that many autonomous features vary for the same model. For example, two vehicles of the same model may have different hardware features for automatic braking, different computer instructions for automatic steering, and/or different artificial intelligence system versions.
  • the current make and model rating may also not account for the extent to which another “driver,” in this case the vehicle itself, is controlling the vehicle.
  • the present embodiments may assess and price insurance risks at least in part based upon autonomous or semi-autonomous vehicle technology that replaces actions of the driver.
  • vehicle-related computer instructions and artificial intelligence may be viewed as a “driver.”
  • (1) data may be captured by a processor (such as via wireless communication) to determine the autonomous or semi-autonomous technology or functionality associated with a specific vehicle that is, or is to be, covered by insurance; (2) the received data may be compared by the processor to a stored baseline of vehicle data (such as actual accident information, and/or autonomous or semi-autonomous vehicle testing data); (3) risk may be identified or assessed by the processor based upon the specific vehicle's ability to make driving decisions and/or avoid or mitigate crashes; (4) an insurance policy may be adjusted (or generated or created), or an insurance premium may be determined by the processor based upon the risk identified that is associated with the specific vehicle's autonomous or semi-autonomous ability or abilities; and/or (5) the insurance policy and/or premium may be presented on a display or otherwise provided to the policyholder or potential customer for their review and/or approval.
  • the method may include additional, fewer, or alternate actions, including those discussed below and elsewhere herein.
  • the method may include evaluating the effectiveness of artificial intelligence and/or vehicle technology in a test environment, and/or using real driving experience.
  • the identification or assessment of risk performed by the method (and/or the processor) may be dependent upon the extent of control and decision making that is assumed by the vehicle, rather than the driver.
  • the identification or assessment of insurance and/or accident-based risk may be dependent upon the ability of the vehicle to use external information (such as vehicle-to-vehicle and vehicle-to-infrastructure communication) to make driving decisions.
  • the risk assessment may further be dependent upon the availability of such external information.
  • a vehicle or vehicle owner
  • a geographical location such as a large city or urban area, where such external information is readily available via wireless communication.
  • a small town or rural area may or may not have such external information available.
  • the information regarding the availability of autonomous or semi-autonomous vehicle technology may be wirelessly transmitted to a remote server for analysis.
  • the remote server may be associated with an insurance provider, vehicle manufacturer, autonomous technology provider, and/or other entity.
  • the driving experience and/or usage of the autonomous or semi-autonomous vehicle technology may be monitored in real time, small timeframes, and/or periodically to provide feedback to the driver, insurance provider, and/or adjust insurance policies or premiums.
  • information may be wirelessly transmitted to the insurance provider, such as from a transceiver associated with a smart car to an insurance provider remote server.
  • Insurance policies including insurance premiums, discounts, and rewards, may be updated, adjusted, and/or determined based upon hardware or software functionality, and/or hardware or software upgrades. Insurance policies, including insurance premiums, discounts, etc. may also be updated, adjusted, and/or determined based upon the amount of usage and/or the type(s) of the autonomous or semi-autonomous technology employed by the vehicle.
  • performance of autonomous driving software and/or sophistication of artificial intelligence may be analyzed for each vehicle.
  • An automobile insurance premium may be determined by evaluating how effectively the vehicle may be able to avoid and/or mitigate crashes and/or the extent to which the driver's control of the vehicle is enhanced or replaced by the vehicle's software and artificial intelligence.
  • artificial intelligence capabilities may be evaluated to determine the relative risk of the insurance policy. This evaluation may be conducted using multiple techniques. Vehicle technology may be assessed in a test environment, in which the ability of the artificial intelligence to detect and avoid potential crashes may be demonstrated experimentally. For example, this may include a vehicle's ability to detect a slow-moving vehicle ahead and/or automatically apply the brakes to prevent a collision.
  • Results from both the test environment and/or actual insurance losses may be compared to the results of other autonomous software packages and/or vehicles lacking autonomous driving technology to determine a relative risk factor (or level of risk) for the technology in question.
  • This risk factor (or level of risk) may be applicable to other vehicles that utilize the same or similar autonomous operation software package(s).
  • Emerging technology such as new iterations of artificial intelligence systems, may be priced by combining its individual test environment assessment with actual losses corresponding to vehicles with similar autonomous operation software packages.
  • the entire vehicle software and artificial intelligence evaluation process may be conducted with respect to various technologies and/or elements that affect driving experience. For example, a fully autonomous vehicle may be evaluated based on its vehicle-to-vehicle communications. A risk factor could then be determined and applied when pricing the vehicle. The driver's past loss experience and/or other driver risk characteristics may not be considered for fully autonomous vehicles, in which all driving decisions are made by the vehicle's artificial intelligence.
  • a separate portion of the automobile insurance premium may be based explicitly on the artificial intelligence software's driving performance and characteristics.
  • the artificial intelligence pricing model may be combined with traditional methods for semi-autonomous vehicles.
  • Insurance pricing for fully autonomous, or driverless, vehicles may be based upon the artificial intelligence model score by excluding traditional rating factors that measure risk presented by the drivers.
  • Evaluation of vehicle software and/or artificial intelligence may be conducted on an aggregate basis or for specific combinations of technology and/or driving factors or elements (as discussed elsewhere herein).
  • the vehicle software test results may be combined with actual loss experience to determine relative risk.
  • FIG. 1 illustrates a block diagram of an exemplary autonomous vehicle insurance system 100 on which the exemplary methods described herein may be implemented.
  • the high-level architecture includes both hardware and software applications, as well as various data communications channels for communicating data between the various hardware and software components.
  • the autonomous vehicle insurance system 100 may be roughly divided into front-end components 102 and back-end components 104 .
  • the front-end components 102 may obtain information regarding a vehicle 108 (e.g., a car, truck, motorcycle, etc.) and the surrounding environment.
  • An on-board computer 114 may utilize this information to operate the vehicle 108 according to an autonomous operation feature or to assist the vehicle operator in operating the vehicle 108 .
  • the front-end components 102 may include one or more sensors 120 installed within the vehicle 108 that may communicate with the on-board computer 114 .
  • the front-end components 102 may further process the sensor data using the on-board computer 114 or a mobile device 110 (e.g., a smart phone, a tablet computer, a special purpose computing device, etc.) to determine when the vehicle is in operation and information regarding the vehicle.
  • the front-end components 102 may communicate with the back-end components 104 via a network 130 .
  • Either the on-board computer 114 or the mobile device 110 may communicate with the back-end components 104 via the network 130 to allow the back-end components 104 to record information regarding vehicle usage.
  • the back-end components 104 may use one or more servers 140 to receive data from the front-end components 102 , determine use and effectiveness of autonomous operation features, determine risk levels or premium price, and/or facilitate purchase or renewal of an autonomous vehicle insurance policy.
  • the front-end components 102 may be disposed within or communicatively connected to one or more on-board computers 114 , which may be permanently or removably installed in the vehicle 108 .
  • the on-board computer 114 may interface with the one or more sensors 120 within the vehicle 108 (e.g., an ignition sensor, an odometer, a system clock, a speedometer, a tachometer, an accelerometer, a gyroscope, a compass, a geolocation unit, a camera, a distance sensor, etc.), which sensors may also be incorporated within or connected to the on-board computer 114 .
  • the front end components 102 may further include a communication component 122 to transmit information to and receive information from external sources, including other vehicles, infrastructure, or the back-end components 104 .
  • the mobile device 110 may supplement the functions performed by the on-board computer 114 described herein by, for example, sending or receiving information to and from the mobile server 140 via the network 130 .
  • the on-board computer 114 may perform all of the functions of the mobile device 110 described herein, in which case no mobile device 110 may be present in the system 100 . Either or both of the mobile device 110 or on-board computer 114 may communicate with the network 130 over links 112 and 118 , respectively. Additionally, the mobile device 110 and on-board computer 114 may communicate with one another directly over link 116 .
  • the mobile device 110 may be either a general-use personal computer, cellular phone, smart phone, tablet computer, or a dedicated vehicle use monitoring device. Although only one mobile device 110 is illustrated, it should be understood that a plurality of mobile devices 110 may be used in some embodiments.
  • the on-board computer 114 may be a general-use on-board computer capable of performing many functions relating to vehicle operation or a dedicated computer for autonomous vehicle operation. Further, the on-board computer 114 may be installed by the manufacturer of the vehicle 108 or as an aftermarket modification or addition to the vehicle 108 . In some embodiments or under certain conditions, the mobile device 110 or on-board computer 114 may function as thin-client devices that outsource some or most of the processing to the server 140 .
  • the sensors 120 may be removably or fixedly installed within the vehicle 108 and may be disposed in various arrangements to provide information to the autonomous operation features.
  • the sensors 120 may be included one or more of a GPS unit, a radar unit, a LIDAR unit, an ultrasonic sensor, an infrared sensor, a camera, an accelerometer, a tachometer, or a speedometer.
  • Some of the sensors 120 e.g., radar, LIDAR, or camera units
  • Other sensors 120 may provide data for determining the location or movement of the vehicle 108 .
  • Other sensors 120 may be directed to the interior or passenger compartment of the vehicle 108 , such as cameras, microphones, pressure sensors, thermometers, or similar sensors to monitor the vehicle operator and/or passengers within the vehicle 108 .
  • Information generated or received by the sensors 120 may be communicated to the on-board computer 114 or the mobile device 110 for use in autonomous vehicle operation.
  • the communication component 122 may receive information from external sources, such as other vehicles or infrastructure.
  • the communication component 122 may also send information regarding the vehicle 108 to external sources.
  • the communication component 122 may include a transmitter and a receiver designed to operate according to predetermined specifications, such as the dedicated short-range communication (DSRC) channel, wireless telephony, Wi-Fi, or other existing or later-developed communications protocols.
  • DSRC dedicated short-range communication
  • the received information may supplement the data received from the sensors 120 to implement the autonomous operation features.
  • the communication component 122 may receive information that an autonomous vehicle ahead of the vehicle 108 is reducing speed, allowing the adjustments in the autonomous operation of the vehicle 108 .
  • the on-board computer 114 may directly or indirectly control the operation of the vehicle 108 according to various autonomous operation features.
  • the autonomous operation features may include software applications or modules implemented by the on-board computer 114 to control the steering, braking, or throttle of the vehicle 108 .
  • the on-board computer 114 may be communicatively connected to the controls or components of the vehicle 108 by various electrical or electromechanical control components (not shown).
  • the vehicle 108 may be operable only through such control components (not shown).
  • the control components may be disposed within or supplement other vehicle operator control components (not shown), such as steering wheels, accelerator or brake pedals, or ignition switches.
  • the front-end components 102 communicate with the back-end components 104 via the network 130 .
  • the network 130 may be a proprietary network, a secure public internet, a virtual private network or some other type of network, such as dedicated access lines, plain ordinary telephone lines, satellite links, cellular data networks, combinations of these. Where the network 130 comprises the Internet, data communications may take place over the network 130 via an Internet communication protocol.
  • the back-end components 104 include one or more servers 140 . Each server 140 may include one or more computer processors adapted and configured to execute various software applications and components of the autonomous vehicle insurance system 100 , in addition to other software applications.
  • the server 140 may further include a database 146 , which may be adapted to store data related to the operation of the vehicle 108 and its autonomous operation features.
  • Such data might include, for example, dates and times of vehicle use, duration of vehicle use, use and settings of autonomous operation features, speed of the vehicle 108 , RPM or other tachometer readings of the vehicle 108 , lateral and longitudinal acceleration of the vehicle 108 , incidents or near collisions of the vehicle 108 , communication between the autonomous operation features and external sources, environmental conditions of vehicle operation (e.g., weather, traffic, road condition, etc.), errors or failures of autonomous operation features, or other data relating to use of the vehicle 108 and the autonomous operation features, which may be uploaded to the server 140 via the network 130 .
  • the server 140 may access data stored in the database 146 when executing various functions and tasks associated with the evaluating feature effectiveness or assessing risk relating to an autonomous vehicle.
  • the autonomous vehicle insurance system 100 is shown to include one vehicle 108 , one mobile device 110 , one on-board computer 114 , and one server 140 , it should be understood that different numbers of vehicles 108 , mobile devices 110 , on-board computers 114 , and/or servers 140 may be utilized.
  • the system 100 may include a plurality of servers 140 and hundreds of mobile devices 110 or on-board computers 114 , all of which may be interconnected via the network 130 .
  • the database storage or processing performed by the one or more servers 140 may be distributed among a plurality of servers 140 in an arrangement known as “cloud computing.” This configuration may provide various advantages, such as enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information. This may in turn support a thin-client embodiment of the mobile device 110 or on-board computer 114 discussed herein.
  • the server 140 may have a controller 155 that is operatively connected to the database 146 via a link 156 .
  • additional databases may be linked to the controller 155 in a known manner.
  • additional databases may be used for autonomous operation feature information, vehicle insurance policy information, and vehicle use information.
  • the controller 155 may include a program memory 160 , a processor 162 (which may be called a microcontroller or a microprocessor), a random-access memory (RAM) 164 , and an input/output (I/O) circuit 166 , all of which may be interconnected via an address/data bus 165 . It should be appreciated that although only one microprocessor 162 is shown, the controller 155 may include multiple microprocessors 162 .
  • the memory of the controller 155 may include multiple RAMs 164 and multiple program memories 160 .
  • the I/O circuit 166 is shown as a single block, it should be appreciated that the I/O circuit 166 may include a number of different types of I/O circuits.
  • the RAM 164 and program memories 160 may be implemented as semiconductor memories, magnetically readable memories, or optically readable memories, for example.
  • the controller 155 may also be operatively connected to the network 130 via a link 135 .
  • the server 140 may further include a number of software applications stored in a program memory 160 .
  • the various software applications on the server 140 may include an autonomous operation information monitoring application 141 for receiving information regarding the vehicle 108 and its autonomous operation features, a feature evaluation application 142 for determining the effectiveness of autonomous operation features under various conditions, a compatibility evaluation application 143 for determining the effectiveness of combinations of autonomous operation features, a risk assessment application 144 for determining a risk category associated with an insurance policy covering an autonomous vehicle, and an autonomous vehicle insurance policy purchase application 145 for offering and facilitating purchase or renewal of an insurance policy covering an autonomous vehicle.
  • the various software applications may be executed on the same computer processor or on different computer processors.
  • FIG. 2 illustrates a block diagram of an exemplary mobile device 110 or an exemplary on-board computer 114 consistent with the system 100 .
  • the mobile device 110 or on-board computer 114 may include a display 202 , a GPS unit 206 , a communication unit 220 , an accelerometer 224 , one or more additional sensors (not shown), a user-input device (not shown), and/or, like the server 140 , a controller 204 .
  • the mobile device 110 and on-board computer 114 may be integrated into a single device, or either may perform the functions of both.
  • the on-board computer 114 (or mobile device 110 ) interfaces with the sensors 120 to receive information regarding the vehicle 108 and its environment, which information is used by the autonomous operation features to operate the vehicle 108 .
  • the controller 204 may include a program memory 208 , one or more microcontrollers or microprocessors (MP) 210 , a RAM 212 , and an I/O circuit 216 , all of which are interconnected via an address/data bus 214 .
  • the program memory 208 includes an operating system 226 , a data storage 228 , a plurality of software applications 230 , and/or a plurality of software routines 240 .
  • the operating system 226 may include one of a plurality of general purpose or mobile platforms, such as the AndroidTM, iOS®, or Windows® systems, developed by Google Inc., Apple Inc., and Microsoft Corporation, respectively.
  • the operating system 226 may be a custom operating system designed for autonomous vehicle operation using the on-board computer 114 .
  • the data storage 228 may include data such as user profiles and preferences, application data for the plurality of applications 230 , routine data for the plurality of routines 240 , and other data related to the autonomous operation features.
  • the controller 204 may also include, or otherwise be communicatively connected to, other data storage mechanisms (e.g., one or more hard disk drives, optical storage drives, solid state storage devices, etc.) that reside within the vehicle 108 .
  • FIG. 2 depicts only one microprocessor 210
  • the controller 204 may include multiple microprocessors 210 .
  • the memory of the controller 204 may include multiple RAMs 212 and multiple program memories 208 .
  • FIG. 2 depicts the I/O circuit 216 as a single block, the I/O circuit 216 may include a number of different types of I/O circuits.
  • the controller 204 may implement the RAMs 212 and the program memories 208 as semiconductor memories, magnetically readable memories, or optically readable memories, for example.
  • the one or more processors 210 may be adapted and configured to execute any of one or more of the plurality of software applications 230 or any one or more of the plurality of software routines 240 residing in the program memory 204 , in addition to other software applications.
  • One of the plurality of applications 230 may be an autonomous vehicle operation application 232 that may be implemented as a series of machine-readable instructions for performing the various tasks associated with implementing one or more of the autonomous operation features according to the autonomous vehicle operation method 300 .
  • Another of the plurality of applications 230 may be an autonomous communication application 234 that may be implemented as a series of machine-readable instructions for transmitting and receiving autonomous operation information to or from external sources via the communication module 220 .
  • Still another application of the plurality of applications 230 may include an autonomous operation monitoring application 236 that may be implemented as a series of machine-readable instructions for sending information regarding autonomous operation of the vehicle to the server 140 via the network 130 .
  • the plurality of software applications 230 may call various of the plurality of software routines 240 to perform functions relating to autonomous vehicle operation, monitoring, or communication.
  • One of the plurality of software routines 240 may be a configuration routine 242 to receive settings from the vehicle operator to configure the operating parameters of an autonomous operation feature.
  • Another of the plurality of software routines 240 may be a sensor control routine 244 to transmit instructions to a sensor 120 and receive data from the sensor 120 .
  • Still another of the plurality of software routines 240 may be an autonomous control routine 246 that performs a type of autonomous control, such as collision avoidance, lane centering, or speed control.
  • the autonomous vehicle operation application 232 may cause a plurality of autonomous control routines 246 to determine control actions required for autonomous vehicle operation.
  • one of the plurality of software routines 240 may be a monitoring and reporting routine 248 that transmits information regarding autonomous vehicle operation to the server 140 via the network 130 .
  • Yet another of the plurality of software routines 240 may be an autonomous communication routine 250 for receiving and transmitting information between the vehicle 108 and external sources to improve the effectiveness of the autonomous operation features.
  • Any of the plurality of software applications 230 may be designed to operate independently of the software applications 230 or in conjunction with the software applications 230 .
  • the controller 204 of the on-board computer 114 may implement the autonomous vehicle operation application 232 to communicate with the sensors 120 to receive information regarding the vehicle 108 and its environment and process that information for autonomous operation of the vehicle 108 .
  • the controller 204 may further implement the autonomous communication application 234 to receive information for external sources, such as other autonomous vehicles, smart infrastructure (e.g., electronically communicating roadways, traffic signals, or parking structures), or other sources of relevant information (e.g., weather, traffic, local amenities).
  • Some external sources of information may be connected to the controller 204 via the network 130 , such as the server 140 or internet-connected third-party databases (not shown).
  • the autonomous vehicle operation application 232 and the autonomous communication application 234 are shown as two separate applications, it should be understood that the functions of the autonomous operation features may be combined or separated into any number of the software applications 230 or the software routines 240 .
  • the controller 204 may further implement the autonomous operation monitoring application 236 to communicate with the server 140 to provide information regarding autonomous vehicle operation.
  • This may include information regarding settings or configurations of autonomous operation features, data from the sensors 120 regarding the vehicle environment, data from the sensors 120 regarding the response of the vehicle 108 to its environment, communications sent or received using the communication component 122 or the communication unit 220 , operating status of the autonomous vehicle operation application 232 and the autonomous communication application 234 , or commands sent from the on-board computer 114 to the control components (not shown) to operate the vehicle 108 .
  • the information may be received and stored by the server 140 implementing the autonomous operation information monitoring application 141 , and the server 140 may then determine the effectiveness of autonomous operation under various conditions by implementing the feature evaluation application 142 and the compatibility evaluation application 143 .
  • the effectiveness of autonomous operation features and the extent of their use may be further used to determine risk associated with operation of the autonomous vehicle by the server 140 implementing the risk assessment application 144 .
  • the mobile device 110 or the on-board computer 114 may include additional sensors, such as the GPS unit 206 or the accelerometer 224 , which may provide information regarding the vehicle 108 for autonomous operation and other purposes.
  • the communication unit 220 may communicate with other autonomous vehicles, infrastructure, or other external sources of information to transmit and receive information relating to autonomous vehicle operation.
  • the communication unit 220 may communicate with the external sources via the network 130 or via any suitable wireless communication protocol network, such as wireless telephony (e.g., GSM, CDMA, LTE, etc.), Wi-Fi (802.11 standards), WiMAX, Bluetooth, infrared or radio frequency communication, etc.
  • the communication unit 220 may provide input signals to the controller 204 via the I/O circuit 216 .
  • the communication unit 220 may also transmit sensor data, device status information, control signals, or other output from the controller 204 to one or more external sensors within the vehicle 108 , mobile devices 110 , on-board computers 114 , or servers 140 .
  • the mobile device 110 or the on-board computer 114 may include a user-input device (not shown) for receiving instructions or information from the vehicle operator, such as settings relating to an autonomous operation feature.
  • the user-input device may include a “soft” keyboard that is displayed on the display 202 , an external hardware keyboard communicating via a wired or a wireless connection (e.g., a Bluetooth keyboard), an external mouse, a microphone, or any other suitable user-input device.
  • the user-input device may also include a microphone capable of receiving user voice input.
  • FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicle operation method 300 , which may be implemented by the autonomous vehicle insurance system 100 .
  • the method 300 may begin at block 302 when the controller 204 receives a start signal.
  • the start signal may be a command from the vehicle operator through the user-input device to enable or engage one or more autonomous operation features of the vehicle 108 .
  • the vehicle operator 108 may further specify settings or configuration details for the autonomous operation features.
  • the settings may relate to one or more destinations, route preferences, fuel efficiency preferences, speed preferences, or other configurable settings relating to the operation of the vehicle 108 .
  • fully autonomous vehicles may include additional features or settings permitting them to operate without passengers or vehicle operators within the vehicle.
  • a fully autonomous vehicle may receive an instruction to find a parking space within the general vicinity, which the vehicle may do without the vehicle operator. The vehicle may then be returned to a selected location by a request from the vehicle operator via a mobile device 110 or otherwise. This feature may further be adapted to return a fully autonomous vehicle if lost or stolen.
  • the settings may include enabling or disabling particular autonomous operation features, specifying thresholds for autonomous operation, specifying warnings or other information to be presented to the vehicle operator, specifying autonomous communication types to send or receive, specifying conditions under which to enable or disable autonomous operation features, or specifying other constraints on feature operation.
  • a vehicle operator may set the maximum speed for an adaptive cruise control feature with automatic lane centering.
  • the settings may further include a specification of whether the vehicle 108 should be operating as a fully or partially autonomous vehicle.
  • the start signal may consist of a request to perform a particular task (e.g., autonomous parking) or to enable a particular feature (e.g., autonomous braking for collision avoidance).
  • the start signal may be generated automatically by the controller 204 based upon predetermined settings (e.g., when the vehicle 108 exceeds a certain speed or is operating in low-light conditions).
  • the controller 204 may generate a start signal when communication from an external source is received (e.g., when the vehicle 108 is on a smart highway or near another autonomous vehicle).
  • the controller 204 After receiving the start signal at block 302 , the controller 204 receives sensor data from the sensors 120 during vehicle operation at block 304 . In some embodiments, the controller 204 may also receive information from external sources through the communication component 122 or the communication unit 220 . The sensor data may be stored in the RAM 212 for use by the autonomous vehicle operation application 232 . In some embodiments, the sensor data may be recorded in the data storage 228 or transmitted to the server 140 via the network 130 . The sensor data may alternately either be received by the controller 204 as raw data measurements from one of the sensors 120 or may be preprocessed by the sensor 120 prior to being received by the controller 204 . For example, a tachometer reading may be received as raw data or may be preprocessed to indicate vehicle movement or position. As another example, a sensor 120 comprising a radar or LIDAR unit may include a processor to preprocess the measured signals and send data representing detected objects in 3-dimensional space to the controller 204 .
  • the autonomous vehicle operation application 232 or other applications 230 or routines 240 may cause the controller 204 to process the received sensor data at block 306 in accordance with the autonomous operation features.
  • the controller 204 may process the sensor data to determine whether an autonomous control action is required or to determine adjustments to the controls of the vehicle 108 .
  • the controller 204 may receive sensor data indicating a decreasing distance to a nearby object in the vehicle's path and process the received sensor data to determine whether to begin braking (and, if so, how abruptly to slow the vehicle 108 ).
  • the controller 204 may process the sensor data to determine whether the vehicle 108 is remaining with its intended path (e.g., within lanes on a roadway).
  • the controller 204 may determine appropriate adjustments to the controls of the vehicle to maintain the desired bearing. If the vehicle 108 is moving within the desired path, the controller 204 may nonetheless determine whether adjustments are required to continue following the desired route (e.g., following a winding road). Under some conditions, the controller 204 may determine to maintain the controls based upon the sensor data (e.g., when holding a steady speed on a straight road).
  • the controller 204 may cause the control components of the vehicle 108 to adjust the operating controls of the vehicle to achieve desired operation at block 310 .
  • the controller 204 may send a signal to open or close the throttle of the vehicle 108 to achieve a desired speed.
  • the controller 204 may control the steering of the vehicle 108 to adjust the direction of movement.
  • the vehicle 108 may transmit a message or indication of a change in velocity or position using the communication component 122 or the communication module 220 , which signal may be used by other autonomous vehicles to adjust their controls.
  • the controller 204 may also log or transmit the autonomous control actions to the server 140 via the network 130 for analysis.
  • the controller 204 may continue to receive and process sensor data at blocks 304 and 306 until an end signal is received by the controller 204 at block 312 .
  • the end signal may be automatically generated by the controller 204 upon the occurrence of certain criteria (e.g., the destination is reached or environmental conditions require manual operation of the vehicle 108 by the vehicle operator).
  • the vehicle operator may pause, terminate, or disable the autonomous operation feature or features using the user-input device or by manually operating the vehicle's controls, such as by depressing a pedal or turning a steering instrument.
  • the controller 204 may either continue vehicle operation without the autonomous features or may shut off the vehicle 108 , depending upon the circumstances.
  • the controller 204 may alert the vehicle operator in advance of returning to manual operation.
  • the alert may include a visual, audio, or other indication to obtain the attention of the vehicle operator.
  • the controller 204 may further determine whether the vehicle operator is capable of resuming manual operation before terminating autonomous operation. If the vehicle operator is determined not be capable of resuming operation, the controller 204 may cause the vehicle to stop or take other appropriate action.
  • FIG. 4A is a flow diagram depicting an exemplary autonomous vehicle operation monitoring method 400 , which may be implemented by the autonomous vehicle insurance system 100 .
  • the method 400 monitors the operation of the vehicle 108 and transmits information regarding the vehicle 108 to the server 140 , which information may then be used to determine autonomous operation feature effectiveness or usage rates to assess risk and price vehicle insurance policy premiums.
  • the method 400 may be used both for testing autonomous operation features in a controlled environment of for determining feature use by an insured party.
  • the method 400 may be implemented whenever the vehicle 108 is in operation (manual or autonomous) or only when the autonomous operation features are enabled.
  • the method 400 may likewise be implemented as either a real-time process, in which information regarding the vehicle 108 is communicated to the server 140 while monitoring is ongoing, or as a periodic process, in which the information is stored within the vehicle 108 and communicated to the server 140 at intervals (e.g., upon completion of a trip or when an incident occurs).
  • the method 400 may communicate with the server 140 in real-time when certain conditions exist (e.g., when a sufficient data connection through the network 130 exists or when no roaming charges would be incurred).
  • the method 400 may begin at block 402 when the controller 204 receives an indication of vehicle operation.
  • the indication may be generated when the vehicle 108 is started or when an autonomous operation feature is enabled by the controller 204 or by input from the vehicle operator.
  • the controller 204 may create a timestamp at block 404 .
  • the timestamp may include information regarding the date, time, location, vehicle environment, vehicle condition, and autonomous operation feature settings or configuration information. The date and time may be used to identify one vehicle trip or one period of autonomous operation feature use, in addition to indicating risk levels due to traffic or other factors.
  • the additional location and environmental data may include information regarding the position of the vehicle 108 from the GPS unit 206 and its surrounding environment (e.g., road conditions, weather conditions, nearby traffic conditions, type of road, construction conditions, presence of pedestrians, presence of other obstacles, availability of autonomous communications from external sources, etc.).
  • Vehicle condition information may include information regarding the type, make, and model of the vehicle 108 , the age or mileage of the vehicle 108 , the status of vehicle equipment (e.g., tire pressure, non-functioning lights, fluid levels, etc.), or other information relating to the vehicle 108 .
  • the timestamp may be recorded on the client device 114 , the mobile device 110 , or the server 140 .
  • the autonomous operation feature settings may correspond to information regarding the autonomous operation features, such as those described above with reference to the autonomous vehicle operation method 300 .
  • the autonomous operation feature configuration information may correspond to information regarding the number and type of the sensors 120 , the disposition of the sensors 120 within the vehicle 108 , the one or more autonomous operation features (e.g., the autonomous vehicle operation application 232 or the software routines 240 ), autonomous operation feature control software, versions of the software applications 230 or routines 240 implementing the autonomous operation features, or other related information regarding the autonomous operation features.
  • the configuration information may include the make and model of the vehicle 108 (indicating installed sensors 120 and the type of on-board computer 114 ), an indication of a malfunctioning or obscured sensor 120 in part of the vehicle 108 , information regarding additional after-market sensors 120 installed within the vehicle 108 , a software program type and version for a control program installed as an application 230 on the on-board computer 114 , and software program types and versions for each of a plurality of autonomous operation features installed as applications 230 or routines 240 in the program memory 208 of the on-board computer 114 .
  • the sensors 120 may generate sensor data regarding the vehicle 108 and its environment. In some embodiments, one or more of the sensors 120 may preprocess the measurements and communicate the resulting processed data to the on-board computer 114 .
  • the controller 204 may receive sensor data from the sensors 120 at block 406 .
  • the sensor data may include information regarding the vehicle's position, speed, acceleration, direction, and responsiveness to controls.
  • the sensor data may further include information regarding the location and movement of obstacles or obstructions (e.g., other vehicles, buildings, barriers, pedestrians, animals, trees, or gates), weather conditions (e.g., precipitation, wind, visibility, or temperature), road conditions (e.g., lane markings, potholes, road material, traction, or slope), signs or signals (e.g., traffic signals, construction signs, building signs or numbers, or control gates), or other information relating to the vehicle's environment.
  • sensors 120 may indicate the number of passengers within the vehicle 108 , including an indication of whether the vehicle is entirely empty.
  • the controller 204 may receive autonomous communication data from the communication component 122 or the communication module 220 at block 408 .
  • the communication data may include information from other autonomous vehicles (e.g., sudden changes to vehicle speed or direction, intended vehicle paths, hard braking, vehicle failures, collisions, or maneuvering or stopping capabilities), infrastructure (road or lane boundaries, bridges, traffic signals, control gates, or emergency stopping areas), or other external sources (e.g., map databases, weather databases, or traffic and accident databases).
  • the communication data may be combined with the sensor data received at block 406 to obtain a more robust understanding of the vehicle environment.
  • the server 140 or the controller 204 may combine sensor data indicating frequent changes in speed relative to tachometric data with map data relating to a road upon which the vehicle 108 is traveling to determine that the vehicle 108 is in an area of hilly terrain.
  • weather data indicating recent snowfall in the vicinity of the vehicle 108 may be combined with sensor data indicating frequent slipping or low traction to determine that the vehicle 108 is traveling on a snow-covered or icy road.
  • the controller 204 may process the sensor data, the communication data, and the settings or configuration information to determine whether an incident has occurred.
  • an “incident” is an occurrence during operation of an autonomous vehicle outside of normal safe operating conditions, such that one or more of the following occurs: (i) there is an interruption of vehicle operation, (ii) there is damage to the vehicle or other property, (iii) there is injury to a person, and/or (iv) the conditions require action to be taken by a vehicle operator, autonomous operation feature, pedestrian, or other party to avoid damage or injury.
  • Incidents may include collisions, hard braking, hard acceleration, evasive maneuvering, loss of traction, detection of objects within a threshold distance from the vehicle 108 , alerts presented to the vehicle operator, component failure, inconsistent readings from sensors 120 , or attempted unauthorized access to the on-board computer by external sources. Incidents may also include accidents, vehicle breakdowns, flat tires, empty fuel tanks, or medical emergencies.
  • the controller 204 may anticipate or project an expected incident based upon sensor or external data, allowing the controller 204 to send control signals to minimize the negative effects of the incident. For example, the controller 204 may cause the vehicle 108 to slow and move to the shoulder of a road immediately before running out of fuel. As another example, adjustable seats within the vehicle 108 may be adjusted to better position vehicle occupants in anticipation of a collision.
  • information regarding the incident and the vehicle status may be recorded at block 414 , either in the data storage 228 or the database 146 .
  • the information recorded at block 414 may include sensor data, communication data, and settings or configuration information prior to, during, and immediately following the incident.
  • the information may further include a determination of whether the vehicle 108 has continued operating (either autonomously or manually) or whether the vehicle 108 is capable of continuing to operate in compliance with applicable safety and legal requirements. If the controller 204 determines that the vehicle 108 has discontinued operation or is unable to continue operation at block 416 , the method 400 may terminate. If the vehicle 108 continues operation, then the method 400 may continue at block 418 .
  • FIG. 4B illustrates an alternative portion of the method 400 following an incident.
  • the controller 204 or the server 140 may record status and operating information at block 414 , as above.
  • the incident may interrupt communication between the vehicle 108 and the server 140 via network 130 , such that not all information typically recorded will be available for recordation and analysis by the server 140 .
  • the server 140 or the controller 204 may determine whether assistance may be needed at the location of the vehicle 108 at block 430 .
  • the controller may determine that a head-on collision has occurred based on sensor data (e.g., airbag deployment, automatic motor shut-off, LIDAR data indicating a collision, etc.) and may further determine based on information regarding the speed of the vehicle 108 and other information that medical, police, and/or towing services will be necessary.
  • the determination that assistance is needed at block 430 may further include a determination of types of assistance needed (e.g., police, ambulance, fire, towing, vehicle maintenance, fuel delivery, etc.).
  • the determination at block 430 may be supplemented by a verification attempt, such as a phone call or communication through the on-board computer 114 . Where the verification attempt indicates assistance is required or communication attempts fail, the server 140 or controller 204 would then determine that assistance is needed, as described above. For example, when assistance is determined to be needed at block 430 following an accident involving the vehicle 108 , the server 140 may direct an automatic telephone call to a mobile telephone number associated with the vehicle 108 or the vehicle operator. If no response is received, or if the respondent indicates assistance is required, the server 140 may proceed to cause a request for assistance to be generated.
  • a verification attempt such as a phone call or communication through the on-board computer 114 .
  • the server 140 or controller 204 would then determine that assistance is needed, as described above. For example, when assistance is determined to be needed at block 430 following an accident involving the vehicle 108 , the server 140 may direct an automatic telephone call to a mobile telephone number associated with the vehicle 108 or the vehicle operator. If no response is received, or
  • the controller 204 or the server 140 may send a request for assistance at block 434 .
  • the request may include information regarding the vehicle 108 , such as the vehicle's location, the type of assistance required, other vehicles involved in the incident, the pedestrians involved in the incident, vehicle operators or passengers involved in the incident, and/or other relevant information.
  • the request for assistance may include telephonic, data, or other requests to one or more emergency or vehicular service providers (e.g., local police, fire departments, state highway patrols, emergency medical services, public or private ambulance services, hospitals, towing companies, roadside assistance services, vehicle rental services, local claims representative offices, etc.).
  • emergency or vehicular service providers e.g., local police, fire departments, state highway patrols, emergency medical services, public or private ambulance services, hospitals, towing companies, roadside assistance services, vehicle rental services, local claims representative offices, etc.
  • the controller 204 or the server 140 may next determine whether the vehicle is operational at block 416 , as described above. The method 400 may then end or continue as indicated in FIG. 4A .
  • the controller 204 may further determine information regarding the likely cause of a collision or other incident.
  • the server 140 may receive information regarding an incident from the on-board computer 114 and determine relevant additional information regarding the incident from the sensor data.
  • the sensor data may be used to determine the points of impact on the vehicle 108 and another vehicle involved in a collision, the relative velocities of each vehicle, the road conditions at the time of the incident, and the likely cause or the party likely at fault. This information may be used to determine risk levels associated with autonomous vehicle operation, as described below, even where the incident is not reported to the insurer.
  • FIG. 5 illustrates a flow diagram of an exemplary autonomous operation feature evaluation method 500 for determining the effectiveness of autonomous operation features, which may be implemented by the autonomous vehicle insurance system 100 .
  • the method 500 begins by monitoring and recording the responses of an autonomous operation feature in a test environment at block 502 .
  • the test results are then used to determine a plurality of risk levels for the autonomous operation feature corresponding to the effectiveness of the feature in situations involving various conditions, configurations, and settings at block 504 .
  • the method 500 may refine or adjust the risk levels based upon operating data and actual losses for insured autonomous vehicles operation outside the test environment in blocks 506 - 510 .
  • the method 500 may be performed to evaluate each of any number of autonomous operation features or combinations of autonomous operation features.
  • the method 500 may be implemented for a plurality of autonomous operation features concurrently on multiple servers 140 or at different times on one or more servers 140 .
  • the effectiveness of an autonomous operation feature is tested in a controlled testing environment by presenting test conditions and recording the responses of the feature.
  • the testing environment may include a physical environment in which the autonomous operation feature is tested in one or more vehicles 108 . Additionally, or alternatively, the testing environment may include a virtual environment implemented on the server 140 or another computer system in which the responses of the autonomous operation feature are simulated. Physical or virtual testing may be performed for a plurality of vehicles 108 and sensors 120 or sensor configurations, as well as for multiple settings of the autonomous operation feature.
  • the compatibility or incompatibility of the autonomous operation feature with vehicles 108 , sensors 120 , communication units 122 , on-board computers 114 , control software, or other autonomous operation features may be tested by observing and recording the results of a plurality of combinations of these with the autonomous operation feature.
  • an autonomous operation feature may perform well in congested city traffic conditions, but that will be of little use if it is installed in an automobile with control software that operates only above 30 miles per hour.
  • some embodiments may further test the response of autonomous operation features or control software to attempts at unauthorized access (e.g., computer hacking attempts), which results may be used to determine the stability or reliability of the autonomous operation feature or control software.
  • the test results may be recorded by the server 140 .
  • the test results may include responses of the autonomous operation feature to the test conditions, along with configuration and setting data, which may be received by the on-board computer 114 and communicated to the server 140 .
  • the on-board computer 114 may be a special-purpose computer or a general-purpose computer configured for generating or receiving information relating to the responses of the autonomous operation feature to test scenarios.
  • additional sensors may be installed within the vehicle 108 or in the vehicle environment to provide additional information regarding the response of the autonomous operation feature to the test conditions, which additional sensors may not provide sensor data to the autonomous operation feature.
  • new versions of previously tested autonomous operation features may not be separately tested, in which case the block 502 may not be present in the method 500 .
  • the server 140 may determine the risk levels associated with the new version by reference to the risk profile of the previous version of the autonomous operation feature in block 504 , which may be adjusted based upon actual losses and operating data in blocks 506 - 510 .
  • each version of the autonomous operation feature may be separately tested, either physically or virtually.
  • a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version.
  • FIG. 6 illustrates a flow diagram of an exemplary autonomous operation feature testing method 600 for presenting test conditions to an autonomous operation feature and observing and recording responses to the test conditions in accordance with the method 500 .
  • the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature).
  • the scope of the testing may include parameters such as configurations, settings, vehicles 108 , sensors 120 , communication units 122 , on-board computers 114 , control software, other autonomous operation features, or combinations of these parameters to be tested.
  • the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602 .
  • the test system may be a vehicle 108 or a computer simulation, as discussed above.
  • the autonomous operation feature or the test system may be configured to provide the desired parameter inputs to the autonomous operation feature.
  • the controller 204 may disable a number of sensors 120 or may provide only a subset of available sensor data to the autonomous operation feature for the purpose of testing the feature's response to certain parameters.
  • test inputs are presented to the autonomous operation feature, and responses of the autonomous operation feature are observed at block 608 .
  • the test inputs may include simulated data presented by the on-board computer 114 or sensor data from the sensors 120 within the vehicle 108 .
  • the vehicle 108 may be controlled within a physical test environment by the on-board computer 114 to present desired test inputs through the sensors 120 .
  • the on-board computer 114 may control the vehicle 108 to maneuver near obstructions or obstacles, accelerate, or change directions to trigger responses from the autonomous operation feature.
  • the test inputs may also include variations in the environmental conditions of the vehicle 108 , such as by simulating weather conditions that may affect the performance of the autonomous operation feature (e.g., snow or ice cover on a roadway, rain, or gusting crosswinds, etc.).
  • variations in the environmental conditions of the vehicle 108 such as by simulating weather conditions that may affect the performance of the autonomous operation feature (e.g., snow or ice cover on a roadway, rain, or gusting crosswinds, etc.).
  • additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles. These additional vehicles may likewise be controlled by on-board computers or remotely by the server 140 through the network 130 .
  • the additional vehicles may transmit autonomous communication information to the vehicle 108 , which may be received by the communication component 122 or the communication unit 220 and presented to the autonomous operation feature by the on-board computer 114 .
  • the response of the autonomous operation feature may be tested with and without autonomous communications from external sources.
  • the responses of the autonomous operation feature may be observed as output signals from the autonomous operation feature to the on-board computer 114 or the vehicle controls. Additionally, or alternatively, the responses may be observed by sensor data from the sensors 120 and additional sensors within the vehicle 108 or placed within the vehicle environment.
  • the observed responses of the autonomous operation feature are recorded for use in determining effectiveness of the feature.
  • the responses may be recorded in the data storage 228 of the on-board computer 114 or in the database 146 of the server 140 . If the responses are stored on the on-board computer 114 during testing, the results may be communicated to the server 140 via the network either during or after completion of testing.
  • the on-board computer 114 or the server 140 may determine whether the additional sets of parameters remain for which the autonomous operation feature is to be tested, as determined in block 602 . When additional parameter sets are determined to remain at block 612 , they are separately tested according to blocks 604 - 610 . When no additional parameter sets are determined to exist at block 612 , the method 600 terminates.
  • testing of autonomous features within a virtual environment may include the presentation of electrical signals mimicking signals generated by one or more sensors in a plurality of operating scenarios at block 606 .
  • a control unit or on-board computer 114 removed from a vehicle may be connected to one or more sensor input simulators (e.g., a computer or computer-controlled signal generator) that present input signals to the control unit or on-board computer that correspond to signals that would be received from the sensors 120 in the vehicle 108 under certain conditions.
  • the same or another computer may be connected to the control unit or on-board computer 114 to receive and record the control outputs determined by the one or more autonomous operation features in response to the simulated sensor input at blocks 608 and 610 .
  • the virtual test environment may include a simulation of an autonomous (and/or semi-autonomous) operation feature running on a general-purpose or special-purpose computer system.
  • the autonomous operation feature may include one or more software routines, processes, applications, or modules that implement the autonomous operation feature to generate control signals for a vehicle when executed on a general-purpose or special-purpose processor.
  • an adaptive cruise control feature may include a software routine to monitor the speed of the vehicle using a combination of speedometer and other sensor data, as well as a software routine to determine the distance of the vehicle from obstacles or other vehicles in the vehicle's path using LIDAR and autonomous communication data.
  • the adaptive cruise control feature may further include a control software routine to determine adjustments to the vehicle's speed to maintain a safe distance from other vehicles and obstacles then generate a control signal to maintain or adjust the throttle of the vehicle.
  • the software routines of the autonomous operation feature may be executed on a processor of a computer system not connected to any vehicle, in which case test input signals simulating signals from sensors within a vehicle may be presented to the software routines to test the routines' responses.
  • a process of the computer system may execute instructions causing the processor to access a set of simulated sensor test input signals at block 606 , determine a response (such as one or more output test signals) of the autonomous operation feature based on the executable instructions representing one or more software routines of the autonomous operation feature at block 608 , and record the determined response at block 610 .
  • virtual testing may further include predicting a response of a vehicle to output test signals generated by the software routines of the tested autonomous operation feature.
  • the simulated sensor inputs and/or test input signals may include sets of data corresponding to generated or recorded signals from a plurality of sensors.
  • the computer system may access a first set of sensor data and sequentially update the sensor data using the determined responses of the simulated autonomous operation feature.
  • the test input signals may include input signals stored in a database and accessed by a testing program or application for presentation to the software routines of the autonomous operation feature being tested.
  • the computer system may generate sensor input signals based on a simulation of a physical test environment and update the virtual test environment based on the determined responses of the autonomous operation features.
  • a virtual testing program or application may further control the virtual testing process and may simulate the operation of a virtual vehicle within a virtual test environment.
  • the virtual test environment may include a computer simulation of an environment in which an autonomous vehicle may be expected to operate (e.g., a congested highway, a city street with multiple intersections, etc.).
  • the virtual testing program or application may, thus, generate a simulation of the operation of the virtual vehicle in the virtual test environment, including vehicle attributes such as position, speed, or momentum.
  • the virtual testing program may further simulate the presence or operation of other vehicles, traffic control devices, obstacles, pedestrians, or other relevant features of a vehicle environment. Based upon these simulated features of the virtual vehicle environment, one or more simulated sensor readings or data may be determined, which may further be used to determine one or more test input signals to the software routines of the autonomous operation feature. The virtual testing program or application may then present the test input signals to the software routines and cause test output signals to be generated in response to the test input signals. From these test output signals, the virtual testing program may then predict the response of the virtual vehicle to the test output signals, including the responses of the virtual vehicle in relation to other virtual vehicles or other features in the simulated virtual test environment.
  • the input data may include sensor data recorded during operation of an autonomous (and/or semi-autonomous) vehicle 108 , which may include operation by a vehicle operator or by other autonomous (and/or semi-autonomous) operation features.
  • a vehicle operator may choose to operate the vehicle 108 manually under some conditions (e.g., snow, fog, or construction), or the autonomous operation features may not support autonomous operation under such conditions.
  • the sensors 120 of the vehicle 108 may, however, continue to collect and record data regarding the surrounding environment.
  • the sensor data may then be used to simulate autonomous operation feature responses (i.e., the control signals the autonomous operation feature would have generated had it been in control of the vehicle).
  • the data and responses may be stored or transmitted via the network 130 , and the responses of the autonomous operation features may be determined at the time of operation or at a later time.
  • the effectiveness of one or more autonomous operation features may be further used to determine one or more risk levels or risk profiles associated with the autonomous operation features.
  • the test output responses generated by the software routines of the autonomous operation features or the predicted responses of the virtual vehicle to the test output responses may be compared with other similar virtual test data related to other autonomous operation features.
  • actual observed operating data regarding the one or more other autonomous operation features disposed within a plurality of other vehicles operating outside the virtual test environment may also be compared with the virtual test data using known statistical modeling techniques.
  • actual loss data for vehicles operating outside the virtual test environment and having the other autonomous operating features may further be compared with the virtual test data to better assess the risk levels and risk profiles associated with the tested autonomous operating features.
  • test scenarios may be automatically run to evaluate autonomous (and/or semi-autonomous) operation feature performance under a variety of conditions without input from a user or vehicle operator.
  • a new autonomous operation feature or a software update including a new version of an autonomous operation feature may be tested in a virtual test environment prior to installation within autonomous vehicles, allowing immediate adjustment of risk levels or risk profiles for vehicles using the new autonomous operation feature or version.
  • adjustments to risks associated with autonomous operation features may be made without reference to actual loss data relating to the specific autonomous operation features.
  • Such advantages must be weighed against the limitations of virtual testing, however, because the test results are limited by the quality of the virtual test environment. It will be readily apparent that responses from physical and virtual test environments may be combined in order to determine the performance and risk levels associated with autonomous operation features.
  • the server 140 may determine a baseline risk profile for the autonomous operation feature from the recorded test results at block 504 , including a plurality of risk levels corresponding to a plurality of sets of parameters such as configurations, settings, vehicles 108 , sensors 120 , communication units 122 , on-board computers 114 , control software, other autonomous operation features, or combinations of these.
  • the server 140 may determine the risk levels associated with the autonomous operation feature by implementing the feature evaluation application 142 to determine the effectiveness of the feature.
  • the server 140 may further implement the compatibility evaluation application 143 to determine the effectiveness of combinations of features based upon test results and other information.
  • the baseline risk profile may not depend upon the type, make, model, year, or other aspect of the vehicle 108 .
  • the baseline risk profile and adjusted risk profiles may correspond to the effectiveness or risk levels associated with the autonomous operation features across a range of vehicles, disregarding any variations in effectiveness or risk levels associated with operation of the features in different vehicles.
  • FIG. 7 illustrates a flow diagram of an exemplary autonomous feature evaluation method 700 for determining the effectiveness of an autonomous operation feature under a set of environmental conditions, configuration conditions, and settings.
  • the method 700 shows determination of a risk level associated with an autonomous operation feature within one set of parameters, it should be understood that the method 700 may be implemented for any number of sets of parameters for any number of autonomous features or combinations thereof.
  • the server 140 receives the test result data observed and recorded in block 502 for the autonomous operation feature in conjunction with a set of parameters.
  • the rest result data may be received from the on-board computer 114 or from the database 146 .
  • the server 140 may receive reference data for other autonomous operation features in use on insured autonomous vehicles at block 704 , such as test result data and corresponding actual loss or operating data for the other autonomous operation features.
  • the reference data received at block 704 may be limited to data for other autonomous operation features having sufficient similarity to the autonomous operation feature being evaluated, such as those performing a similar function, those with similar test result data, or those meeting a minimum threshold level of actual loss or operating data.
  • the server 140 determines the expected actual loss or operating data for the autonomous operation feature at block 706 .
  • the server 140 may determine the expected actual loss or operating data using known techniques, such as regression analysis or machine learning tools (e.g., neural network algorithms or support vector machines).
  • the expected actual loss or operating data may be determined using any useful metrics, such as expected loss value, expected probabilities of a plurality of collisions or other incidents, expected collisions per unit time or distance traveled by the vehicle, etc.
  • the server 140 may further determine a risk level associated with the autonomous operation feature in conjunction with the set of parameters received in block 702 .
  • the risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning.
  • the risk level may be defined in various alternative ways, including as a probability of loss per unit time or distance traveled, a percentage of collisions avoided, or a score on a fixed scale.
  • the risk level is defined as an effectiveness rating score such that a higher score corresponds to a lower risk of loss associated with the autonomous operation feature.
  • the method 700 may be implemented for each relevant combination of an autonomous operation feature in conjunction with a set of parameters relating to environmental conditions, configuration conditions, and settings. It may be beneficial in some embodiments to align the expected losses or operating data metrics with loss categories for vehicle insurance policies.
  • the plurality of risk levels in the risk profile may be updated or adjusted in blocks 506 - 510 using actual loss and operating data from autonomous vehicles operating in the ordinary course, viz. not in a test environment.
  • the server 140 may receive operating data from one or more vehicles 108 via the network 130 regarding operation of the autonomous operation feature.
  • the operating data may include the operating data discussed above with respect to monitoring method 400 , including information regarding the vehicle 108 , the vehicle's environment, the sensors 120 , communications for external sources, the type and version of the autonomous operation feature, the operation of the feature, the configuration and settings relating to the operation of the feature, the operation of other autonomous operation features, control actions performed by the vehicle operator, or the location and time of operation.
  • the operating data may be received by the server 140 from the on-board computer 114 or the mobile device 110 implementing the monitoring method 400 or from other sources, and the server 140 may receive the operating data either periodically or continually.
  • the server 140 may receive data regarding actual losses on autonomous vehicles that included the autonomous operation feature.
  • This information may include claims filed pursuant to insurance policies, claims paid pursuant to insurance policies, accident reports filed with government agencies, or data from the sensors 120 regarding incidents (e.g., collisions, alerts presented, etc.).
  • This actual loss information may further include details such as date, time, location, traffic conditions, weather conditions, road conditions, vehicle speed, vehicle heading, vehicle operating status, autonomous operation feature configuration and settings, autonomous communications transmitted or received, points of contact in a collision, velocity and movements of other vehicles, or additional information relevant to determining the circumstances involved in the actual loss.
  • the server 140 may process the information received at blocks 506 and 508 to determine adjustments to the risk levels determined at block 504 based upon actual loss and operating data for the autonomous operation feature. Adjustments may be necessary because of factors such as sensor failure, interference disrupting autonomous communication, better or worse than expected performance in heavy traffic conditions, etc.
  • the adjustments to the risk levels may be made by methods similar to those used to determine the baseline risk profile for the autonomous operation feature or by other known methods (e.g., Bayesian updating algorithms).
  • the updating procedure of blocks 506 - 510 may be repeatedly implemented periodically or continually as new data become available to refine and update the risk levels or risk profile associated with the autonomous operation feature. In subsequent iterations, the most recently updated risk profile or risk levels may be adjusted, rather than the initial baseline risk profile or risk levels determined in block 504 .
  • FIGS. 8-10 illustrate flow diagrams of exemplary embodiments of methods for determining risk associated with an autonomous vehicle or premiums for vehicle insurance policies covering an autonomous vehicle.
  • the autonomous vehicle may be a fully autonomous vehicle operating without a vehicle operator's input or presence.
  • the vehicle operator may control the vehicle with or without the assistance of the vehicle's autonomous operation features.
  • the vehicle may be fully autonomous only above a minimum speed threshold or may require the vehicle operator to control the vehicle during periods of heavy precipitation.
  • the autonomous vehicle may perform all relevant control functions using the autonomous operation features under all ordinary operating conditions.
  • the vehicle 108 may operate in either a fully or a partially autonomous state, while receiving or transmitting autonomous communications.
  • the method 800 may be implemented to determine the risk level or premium associated with an insurance policy covering the autonomous vehicle.
  • the method 900 may be implemented to determine the risk level or premium associated with an insurance policy covering the autonomous vehicle, including a determination of the risks associated with the vehicle operator performing manual vehicle operation.
  • the method 1000 may be implemented to determine the risk level or premium associated with an insurance policy covering the autonomous vehicle, including a determination of the expected use of autonomous communication features by external sources in the relevant environment of the vehicle 108 during operation of the vehicle 108 .
  • FIG. 8 illustrates a flow diagram depicting an exemplary embodiment of a fully autonomous vehicle insurance pricing method 800 , which may be implemented by the autonomous vehicle insurance system 100 .
  • the method 800 may be implemented by the server 140 to determine a risk level or price for a vehicle insurance policy covering a fully autonomous vehicle based upon the risk profiles of the autonomous operation features in the vehicle.
  • the risk category or price is determined without reference to factors relating to risks associated with a vehicle operator (e.g., age, experience, prior history of vehicle operation). Instead, the risk and price may be determined based upon the vehicle 108 , the location and use of the vehicle 108 , and the autonomous operation features of the vehicle 108 .
  • the server 140 receives a request to determine a risk category or premium associated with a vehicle insurance policy for a fully autonomous vehicle.
  • the request may be caused by a vehicle operator or other customer or potential customer of an insurer, or by an insurance broker or agent.
  • the request may also be generated automatically (e.g., periodically for repricing or renewal of an existing vehicle insurance policy).
  • the server 140 may generate the request upon the occurrence of specified conditions.
  • the server 140 receives information regarding the vehicle 108 , the autonomous operation features installed within the vehicle 108 , and anticipated or past use of the vehicle 108 .
  • the information may include vehicle information (e.g., type, make, model, year of production, safety features, modifications, installed sensors, on-board computer information, etc.), autonomous operation features (e.g., type, version, connected sensors, compatibility information, etc.), and use information (e.g., primary storage location, primary use, primary operating time, past use as monitored by an on-board computer or mobile device, past use of one or more vehicle operators of other vehicles, etc.).
  • vehicle information e.g., type, make, model, year of production, safety features, modifications, installed sensors, on-board computer information, etc.
  • autonomous operation features e.g., type, version, connected sensors, compatibility information, etc.
  • use information e.g., primary storage location, primary use, primary operating time, past use as monitored by an on-board computer or mobile device, past use of one or more vehicle operators of
  • the information may be provided by a person having an interest in the vehicle, a customer, or a vehicle operator, and/or the information may be provided in response to a request for the information by the server 140 .
  • the server 140 may request or receive the information from one or more databases communicatively connected to the server 140 through the network 130 , which may include databases maintained by third parties (e.g., vehicle manufacturers or autonomous operation feature manufacturers).
  • third parties e.g., vehicle manufacturers or autonomous operation feature manufacturers.
  • information regarding the vehicle 108 may be excluded, in which case the risk or premium determinations below may likewise exclude the information regarding the vehicle 108 .
  • the server 140 may determine the risk profile or risk levels associated with the vehicle 108 based upon the vehicle information and the autonomous operation feature information received at block 804 .
  • the risk levels associated with the vehicle 108 may be determined as discussed above with respect to the method 500 and/or may be determined by looking up in a database the risk level information previously determined. In some embodiments, the information regarding the vehicle may be given little or no weight in determining the risk levels. In other embodiments, the risk levels may be determined based upon a combination of the vehicle information and the autonomous operation information. As with the risk levels associated with the autonomous operation features discussed above, the risk levels associated with the vehicle may correspond to the expected losses or incidents for the vehicle based upon its autonomous operation features, configuration, settings, and/or environmental conditions of operation.
  • a vehicle may have a risk level of 98% effectiveness when on highways during fair weather days and a risk level of 87% effectiveness when operating on city streets at night in moderate rain.
  • a plurality of risk levels associated with the vehicle may be combined with estimates of anticipated vehicle use conditions to determine the total risk associated with the vehicle.
  • the server 140 may determine the expected use of the vehicle 108 in the relevant conditions or with the relevant settings to facilitate determining a total risk for the vehicle 108 .
  • the server 140 may determine expected vehicle use based upon the use information received at block 804 , which may include a history of prior use recorded by the vehicle 108 and/or another vehicle. For example, recorded vehicle use information may indicate that 800/% of vehicle use occurs during weekday rush hours in or near a large city, that 20% occurs on nights and weekends. From this information, the server 140 may determine that 80% (75%, 90%, etc.) of the expected use of the vehicle 108 is in heavy traffic and that 20% (25%, 10%, etc.) is in light traffic.
  • the server 140 may further determine that vehicle use is expected to be 60% on limited access highways and 40% on surface streets. Based upon the vehicle's typical storage location, the server 140 may access weather data for the location to determine expected weather conditions during the relevant times. For example, the server 140 may determine that 20% of the vehicle's operation on surface streets in heavy traffic will occur in rain or snow. In a similar manner, the server 140 may determine a plurality of sets of expected vehicle use parameters corresponding to the conditions of use of the vehicle 108 . These conditions may further correspond to situations in which different autonomous operation features may be engaged and/or may be controlling the vehicle. Additionally, or alternatively, the vehicle use parameters may correspond to different risk levels associated with the autonomous operation features. In some embodiments, the expected vehicle use parameters may be matched to the most relevant vehicle risk level parameters, viz. the parameters corresponding to vehicle risk levels that have the greatest predictive effect and/or explanatory power.
  • the server 140 may use the risk levels determined at block 806 and the expected vehicle use levels determined at block 808 to determine a total expected risk level. To this end, it may be advantageous to attempt to match the vehicle use parameters as closely as possible to the vehicle risk level parameters. For example, the server 140 may determine the risk level associated with each of a plurality of sets of expected vehicle use parameters. In some embodiments, sets of vehicle use parameters corresponding to zero or negligible (e.g., below a predetermined threshold probability) expected use levels may be excluded from the determination for computational efficiency. The server 140 may then weight the risk levels by the corresponding expected vehicle use levels, and aggregate the weighted risk levels to obtain a total risk level for the vehicle 108 . In some embodiments, the aggregated weighted risk levels may be adjusted or normalized to obtain the total risk level for the vehicle 108 . In some embodiments, the total risk level may correspond to a regulatory risk category or class of a relevant insurance regulator.
  • the server 140 may determine one or more premiums for vehicle insurance policies covering the vehicle 108 based upon the total risk level determined at block 810 . These policy premiums may also be determine based upon additional factors, such as coverage type and/or amount, expected cost to repair or replace the vehicle 108 , expected cost per claim for liability in the locations where the vehicle 108 is typically used, discounts for other insurance coverage with the same insurer, and/or other factors unrelated to the vehicle operator.
  • the server 140 may further communicate the one or more policy premiums to a customer, broker, agent, or other requesting person or organization via the network 130 .
  • the server 140 may further store the one or more premiums in the database 146 .
  • FIG. 9 illustrates a flow diagram depicting an exemplary embodiment of a partially autonomous vehicle insurance pricing method 900 , which may be implemented by the autonomous vehicle insurance system 100 in a manner similar to that of the method 800 .
  • the method 900 may be implemented by the server 140 to determine a risk category and/or price for a vehicle insurance policy covering an autonomous vehicle based upon the risk profiles of the autonomous operation features in the vehicle and/or the expected use of the autonomous operation features.
  • the method 900 includes information regarding the vehicle operator, including information regarding the expected use of the autonomous operation features and/or the expected settings of the features under various conditions. Such additional information is relevant where the vehicle operator may control the vehicle 108 under some conditions and/or may determine settings affecting the effectiveness of the autonomous operation features.
  • the server 140 may receive a request to determine a risk category and/or premium associated with a vehicle insurance policy for an autonomous vehicle in a manner similar to block 802 described above.
  • the server 140 likewise receives information regarding the vehicle 108 , the autonomous operation features installed within the vehicle 108 , and/or anticipated or past use of the vehicle 108 .
  • the information regarding anticipated or past use of the vehicle 108 may include information regarding past use of one or more autonomous operation features, and/or settings associated with use of the features. For example, this may include times, road conditions, and/or weather conditions when autonomous operation features have been used, as well as similar information for past vehicle operation when the features have been disabled.
  • the server 140 may receive information related to the vehicle operator, including standard information of a type typically used in actuarial analysis of vehicle operator risk (e.g., age, location, years of vehicle operation experience, and/or vehicle operating history of the vehicle operator).
  • standard information of a type typically used in actuarial analysis of vehicle operator risk e.g., age, location, years of vehicle operation experience, and/or vehicle operating history of the vehicle operator.
  • the server 140 may determine the risk profile or risk levels associated with the vehicle 108 based upon the vehicle information and the autonomous operation feature information received at block 904 .
  • the risk levels associated with the vehicle 108 may be determined as discussed above with respect to the method 500 and/or as further discussed with respect to method 800 .
  • the server 140 may determine the expected manual and/or autonomous use of the vehicle 108 in the relevant conditions and/or with the relevant settings to facilitate determining a total risk for the vehicle 108 .
  • the server 140 may determine expected vehicle use based upon the use information received at block 904 , which may include a history of prior use recorded by the vehicle 108 and/or another vehicle for the vehicle operator.
  • Expected manual and autonomous use of the vehicle 108 may be determined in a manner similar to that discussed above with respect to method 800 , but including an additional determination of the likelihood of autonomous and/or manual operation by the vehicle operation under the various conditions.
  • the server 140 may determine based upon past operating data that the vehicle operator manually controls the vehicle 108 when on a limited-access highway only 20% of the time in all relevant environments, but the same vehicle operator controls the vehicle 60% of the time on surface streets outside of weekday rush hours and 35% of the time on surface streets during weekday rush hours. These determinations may be used to further determine the total risk associated with both manual and/or autonomous vehicle operation.
  • the server 140 may use the risk levels determined at block 908 and the expected vehicle use levels determined at block 910 to determine a total expected risk level, including both manual and autonomous operation of the vehicle 108 .
  • the autonomous operation risk levels may be determined as above with respect to block 810 .
  • the manual operation risk levels may be determined in a similar manner, but the manual operation risk may include risk factors related to the vehicle operator.
  • the manual operation risk may also be determined based upon vehicle use parameters and/or related autonomous operation feature risk levels for features that assist the vehicle operator in safely controlling the vehicle. Such features may include alerts, warnings, automatic braking for collision avoidance, and/or similar features that may provide information to the vehicle operator or take control of the vehicle from the vehicle operator under some conditions.
  • the total risk level for the vehicle and operator may be determined by aggregating the weighted risk levels. As above, the total risk level may be adjusted or normalized, and/or it may be used to determine a risk category or risk class in accordance with regulatory requirements.
  • the server 140 may determine one or more premiums for vehicle insurance policies covering the vehicle 108 based upon the total risk level determined at block 812 . As in method 800 , additional factors may be included in the determination of the policy premiums, and/or the premiums may be adjusted based upon additional factors. The server 140 may further record the premiums or may transmit one or more of the policy premiums to relevant parties.
  • FIG. 10 illustrates a flow diagram depicting an exemplary embodiment of an autonomous vehicle insurance pricing method 1000 for determining risk and/or premiums for vehicle insurance policies covering autonomous vehicles with autonomous communication features, which may be implemented by the autonomous vehicle insurance system 100 .
  • the method 1000 may determine risk levels as without autonomous communication discussed above with reference to methods 800 and/or 900 , then adjust the risk levels based upon the availability and effectiveness of communications between the vehicle 108 and external sources. Similar to environmental conditions, the availability of external sources such as other autonomous vehicles for communication with the vehicle 108 affects the risk levels associated with the vehicle 108 . For example, use of an autonomous communication feature may significantly reduce risk associated with autonomous operation of the vehicle 108 only where other autonomous vehicles also use autonomous communication features to send and/or receive information.
  • the server 140 may receive a request to determine a risk category or premium associated with a vehicle insurance policy for an autonomous vehicle with one or more autonomous communication features in a manner similar to blocks 802 and/or 902 described above.
  • the server 140 likewise receives information regarding the vehicle 108 , the autonomous operation features installed within the vehicle 108 (including autonomous communication features), the vehicle operator, and/or anticipated or past use of the vehicle 108 .
  • the information regarding anticipated or past use of the vehicle 108 may include information regarding locations and times of past use, as well as past use of one or more autonomous communication features. For example, this may include locations, times, and/or details of communication exchanged by an autonomous communication feature, as well as information regarding past vehicle operation when no autonomous communication occurred.
  • This information may be used to determine the past availability of external sources for autonomous communication with the vehicle 108 , facilitating determination of expected future availability of autonomous communication as described below.
  • information regarding the vehicle 108 may be excluded, in which case the risk or premium determinations below may likewise exclude the information regarding the vehicle 108 .
  • the server 140 may determine the risk profile or risk levels associated with the vehicle 108 based upon the vehicle information, the autonomous operation feature information, and/or the vehicle operator information received at block 1004 .
  • the risk levels associated with the vehicle 108 may be determined as discussed above with respect to the method 500 and as further discussed with respect to methods 800 and 900 .
  • the server 140 may determine the risk profile and/or risk levels associated with the vehicle 108 and/or the autonomous communication features. This may include a plurality of risk levels associated with a plurality of autonomous communication levels and/or other parameters relating to the vehicle 108 , the vehicle operator, the autonomous operation features, the configuration and/or setting of the autonomous operation features, and/or the vehicle's environment.
  • the autonomous communication levels may include information regarding the proportion of vehicles in the vehicle's environment that are in autonomous communication with the vehicle 108 , levels of communication with infrastructure, types of communication (e.g., hard braking alerts, full velocity information, etc.), and/or other information relating to the frequency and/or quality of autonomous communications between the autonomous communication feature and the external sources.
  • the server 140 may then determine the expected use levels of the vehicle 108 in the relevant conditions, autonomous operation feature settings, and/or autonomous communication levels to facilitate determining a total risk for the vehicle 108 .
  • the server 140 may determine expected vehicle use based upon the use information received at block 1004 , including expected levels of autonomous communication under a plurality of sets of parameters. For example, the server 140 may determine based upon past operating data that the 50% of the total operating time of the vehicle 108 is likely to occur in conditions where approximately a quarter of the vehicles utilize autonomous communication features, 40% of the total operating time is likely to occur in conditions where a negligible number of vehicles utilize autonomous communication features, and/or 10% is likely to occur in conditions where approximately half of vehicles utilize autonomous communication features.
  • each of the categories in the preceding example may be further divided by other conditions, such as traffic levels, weather, average vehicle speed, presence of pedestrians, location, autonomous operation feature settings, and/or other parameters. These determinations may be used to further determine the total risk associated with autonomous vehicle operation including autonomous communication.
  • the server 140 may use the risk levels determined at block 1010 to determine a total expected risk level for the vehicle 108 including one or more autonomous communication features, in a similar manner to the determination described above in block 810 .
  • the server 140 may weight each of the risk levels corresponding to sets of parameters by the expected use levels corresponding to the same set of parameters.
  • the weighted risk levels may then be aggregated using known techniques to determine the total risk level. As above, the total risk level may be adjusted or normalized, or it may be used to determine a risk category or risk class in accordance with regulatory requirements.
  • the server 140 may determine one or more premiums for vehicle insurance policies covering the vehicle 108 based upon the total risk level determined at block 1012 . As in methods 800 and/or 900 , additional factors may be included in the determination of the policy premiums, and/or the premiums may be adjusted based upon additional factors. The server 140 may further record the premiums and/or may transmit one or more of the policy premiums to relevant parties.
  • the determined risk level or premium associated with one or more insurance policies may be presented by the server 140 to a customer or potential customer as offers for one or more vehicle insurance policies.
  • the customer may view the offered vehicle insurance policies on a display such as the display 202 of the mobile device 110 , select one or more options, and/or purchase one or more of the vehicle insurance policies.
  • the display, selection, and/or purchase of the one or more policies may be facilitated by the server 140 , which may communicate via the network 130 with the mobile device 110 and/or another computer device accessed by the user.
  • any of the preceding embodiments may determine or adjust vehicle insurance coverage types or levels (e.g., deductibles, coverage amounts, etc.) based upon use conditions and/or autonomous (and/or semi-autonomous) operation feature use, configuration, or settings. For example, deductibles or premiums for a level of vehicle insurance coverage for theft of a vehicle may be reduced for policies where a fully autonomous vehicle includes autonomous operation features capable of returning the vehicle if stolen. As another example, coverage levels of a vehicle insurance policy may vary based upon whether an autonomous vehicle contains any passengers or vehicle operators. Additionally, coverage types or levels may be adjusted based upon use levels of the autonomous operation features and/or information regarding a vehicle operator.
  • vehicle insurance coverage types or levels e.g., deductibles, coverage amounts, etc.
  • certain coverage types or levels may be unavailable to a vehicle operator (e.g., inexperienced drivers, drivers with extensive accident histories, etc.), except that such coverage may be available when certain autonomous operation features are enabled or activated.
  • vehicle operators who may be otherwise unable or legally prevented from operating a vehicle e.g., due to blindness, physical disabilities, revocation of an operating license, etc.
  • a computer-implemented method of adjusting or creating an insurance policy may be provided.
  • the method may include: (1) capturing or gathering data, via a processor, to determine an autonomous or semi-autonomous technology or functionality associated with a specific vehicle; (2) comparing the received data, via the processor, to a stored baseline of vehicle data created from (a) actual accident data involving automobiles equipped with the autonomous or semi-autonomous technology or functionality, and/or (b) autonomous or semi-autonomous vehicle testing; (3) identifying (or assessing) accident or collision risk, via the processor, based upon an ability of the autonomous or semi-autonomous technology or functionality associated with the specific vehicle to make driving decisions and/or avoid or mitigate crashes; (4) adjusting or creating an insurance policy, via the processor, based upon the accident or collision risk identified that is based upon the ability of the autonomous or semi-autonomous technology or functionality associated with the specific vehicle; and/or (5) presenting on a display screen, or otherwise providing or communicating, all or a portion of (such as a monthly premium or discount
  • the method may include evaluating, via the processor, an effectiveness of the autonomous or semi-autonomous technology or functionality, and/or an associated artificial intelligence, in a test environment, and/or using real driving experience or information.
  • the identification (or assessment) of accident or collision risk performed by the processor may be dependent upon the extent of control and/or decision making that is assumed by the specific vehicle equipped with the autonomous or semi-autonomous technology or functionality, rather than the human driver. Additionally or alternatively, the identification (or assessment) of accident or collision risk may be dependent upon (a) the ability of the specific vehicle to use external information (such as vehicle-to-vehicle, vehicle-to-infrastructure, and/or infrastructure-to-vehicle wireless communication) to make driving decisions, and/or (b) the availability of such external information, such as may be determined by a geographical region (urban or rural) associated with the specific vehicle or vehicle owner.
  • external information such as vehicle-to-vehicle, vehicle-to-infrastructure, and/or infrastructure-to-vehicle wireless communication
  • Information regarding the autonomous or semi-autonomous technology or functionality associated with the specific vehicle may be wirelessly transmitted to a remote server associated with an insurance provider and/or other third party for analysis.
  • the method may include remotely monitoring an amount or percentage of usage of the autonomous or semi-autonomous technology or functionality by the specific vehicle, and based upon such amount or percentage of usage, (a) providing feedback to the driver and/or insurance provider via wireless communication, and/or (b) adjusting insurance policies or premiums.
  • the present embodiments may relate to data acquisition.
  • Data may be gathered via devices employing wireless communication technology, such as Bluetooth or other IEEE communication standards.
  • a Bluetooth enabled smartphone or mobile device, and/or an in-dash smart and/or communications device may collect data.
  • the data associated with the vehicle, and/or vehicle or driver performance, that is gathered or collected at, or on, the vehicle may be wirelessly transmitted to a remote processor or server, such as a remote processor or server associated with an insurance provider.
  • the mobile device 110 may receive the data from the on-board computer 114 or the sensors 120 , and may transmit the received data to the server 140 via the network 130 , and the data may be stored in the database 146 .
  • the transmitted data may include real-time sensor data, a summary of the sensor data, processed sensor data, operating data, environmental data, communication data, or a log such data.
  • Data may be generated by autonomous or semi-autonomous vehicles and/or vehicle mounted sensors (or smart sensors), and then collected by vehicle mounted equipment or processors, including Bluetooth devices, and/or an insurance provider remote processor or server.
  • vehicle mounted equipment or processors including Bluetooth devices, and/or an insurance provider remote processor or server.
  • the data gathered may be used to analyze vehicle decision making.
  • a processor may be configured to generate data on what an autonomous or semi-autonomous vehicle would have done in a given situation had the driver not taken over manual control/driving of the vehicle or alternative control actions not taken by the autonomous or semi-autonomous operation features. This type of control decision data (related to vehicle decision making) may be useful with respect to analyzing hypothetical situations.
  • an application may interact with a vehicle to receive and/or retrieve data from autonomous or semi-autonomous processors and sensors.
  • the data retrieved may be related to radar, cameras, sensor output, computer instructions or application output.
  • Other data related to a smart vehicle controller, car navigation unit information (including route history information and typical routes taken), GPS unit information, odometer and/or speedometer information, and smart equipment data may also be gathered or collected.
  • the application and/or other computer instructions may be associated with an insurance provider remote processor or server.
  • the control decision data may further include information regarding control decisions generated by one or more autonomous operation features within the vehicle.
  • the operating data and control decision data gathered, collected, and/or acquired may facilitate remote evaluation and/or analysis of what the autonomous or semi-autonomous vehicle was “trying to do” (brake, slow, turn, accelerate, etc.) during operation, as well as what the vehicle actually did do.
  • the data may reveal decisions, and the appropriateness thereof, made by the artificial intelligence or computer instructions associated with one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment.
  • the data may include information related to what the vehicle would have done in a situation if the driver had not taken over (beginning manual vehicle control).
  • control decisions data may include information regarding control decisions not implemented by the autonomous operation features to control the vehicle. This may occur when an autonomous operation feature generates a control decision or associated control signal, but the control decision or signal is prevented from controlling the vehicle because the autonomous feature or function is disabled, the control decision is overridden by the vehicle operator, the control signal would conflict with another control signal generated by another autonomous operation feature, a more preferred control decision is generated, or an error occurs in the on-board computer 114 or the control system of the vehicle.
  • a vehicle operator may disable or constrain the operation of some or all autonomous operation features, such as where the vehicle is operated manually or semi-autonomously.
  • the disabled or constrained autonomous operation features may, however, continue to receive sensor data and generate control decision data that is not implemented.
  • one or more autonomous operation features may generate more than one control decision in a relevant period of time as alternative control decisions.
  • Some of these alternative control decisions may not be selected by the autonomous operation feature or an autonomous operation control system to control the vehicle.
  • such alternative control decisions may be generated based on different sets of sensor or communication data from different sensors 120 or include or excluding autonomous communication data.
  • the alternative control decisions may be generated faster than they can be implemented by the control system of the vehicle, thus preventing all control decisions from being implemented.
  • control decision data In addition to control decision data, other information regarding the vehicle, the vehicle environment, or vehicle operation may be collected, generated, transmitted, received, requested, stored, or recorded in connection with the control decision data.
  • additional operating data including sensor data from the sensors 120 , autonomous communication data from the communication component 122 or the communication module 220 , location data, environmental data, time data, settings data, configuration data, and/or other relevant data may be associated with the control decision data.
  • a database or log may store the control decision data and associated information.
  • the entries in such log or database may include a timestamp indicating the date, time, location, vehicle environment, vehicle condition, autonomous operation feature settings, and/or autonomous operation feature configuration information associated with each entry.
  • Such data may facilitate evaluating the autonomous or semi-autonomous technology, functionality, system, and/or equipment in hypothetical situations and/or may be used to calculate risk, and in turn adjust insurance policies, premiums, discounts, etc.
  • the data gathered may be used to evaluate risk associated with the autonomous or semi-autonomous operation feature or technology at issue.
  • information regarding the operation of the vehicle may be monitored or associated with test data or actual loss data regarding losses associated with insurance policies for other vehicles having the autonomous technology or feature to determine risk levels and/or risk profiles.
  • the control decision data, sensor data, and other operating data discussed above may be used to determine risk levels, loss models, and/or risk profiles associated with one or more autonomous or semi-autonomous operation features.
  • External data may further be used to determine risk, as discussed below.
  • Such determined risk levels may further be used to determine insurance rates, premiums, discounts, or costs as discussed in greater detail below.
  • the data gathered may be used to determine an average distance to another vehicle ahead of, and/or behind, the vehicle during normal use of the autonomous or semi-autonomous vehicle technology, functionality, system, and/or equipment.
  • a safe driving distance to other vehicles on the road may lower the risk of accident.
  • the data gathered may also relate to how quickly the technology, functionality, system, and/or equipment may properly stop or slow a vehicle in response to a light changing from green to yellow, and/or from yellow to red. Timely stopping at traffic lights may also positively impact risk of collision.
  • the data gathered may indicate issues not entirely related to the autonomous or semi-autonomous technology, functionality, system, and/or equipment. For instance, tires spinning and low vehicle speed may be monitored and identified to determine that vehicle movement was being affected by the weather (as compared to the technology, functionality, system, and/or equipment during normal operation). Vehicle tires may spin with little or no vehicle movement in snow, rain, mud, ice, etc.
  • the data gathered may indicate a current version of artificial intelligence or computer instructions that the autonomous or semi-autonomous system or equipment is utilizing.
  • a collision risk factor may be assigned to each version of computer instructions.
  • the insurance provider may then adjust or update insurance policies, premiums, rates, discounts, and/or other insurance-related items based upon the collision risk factor and/or the artificial intelligence or computer instruction versions presently employed by the vehicle (and/or upgrades there to).
  • the decision and operating data gathered may be merged with outside data, such as information related to weather, traffic, construction, and/or other factors, and/or collected from sources besides the vehicle.
  • outside data such as information related to weather, traffic, construction, and/or other factors, and/or collected from sources besides the vehicle.
  • data from outside the vehicle may be combined with the control decision data and other operating data discussed above to determine risks associated with the operation of one or more autonomous or semi-autonomous operation features.
  • External data regarding the vehicle environment may be requested or received via the network 130 and associated with the entries in the log or database based on the timestamp. For example, the location, date, and time of a timestamp may be used to determine weather and traffic conditions in which vehicle operation occurred.
  • Additional external data may include road conditions, weather conditions, nearby traffic conditions, type of road, construction conditions, presence of pedestrians, presence of other obstacles, and/or availability of autonomous communications from external sources.
  • weather may impact certain autonomous or semi-autonomous technology, functionality, system, and/or equipment performance, such as fog, visibility, wind, rain, snow, and/or ice.
  • Certain autonomous or semi-autonomous functionality may have degraded performance: (1) on ice covered roads; (2) during snow or rain, and/or on snow or rain covered roads; (3) during poor visibility conditions, such as foggy weather; (4) in “stop and go” traffic, such as during rush hour traffic, or slow moving traffic through high construction areas or downtown areas; and/or (5) caused by other factors.
  • the system and method may consider the geographical area associated with the user, or the owner or operator of a vehicle. For instance, rain mitigation functionality or technology for vehicles may be pertinent to reducing the amount of accidents and/or the severity of such accidents in areas of high rain fall, such as the Pacific Northwest or Florida. On the other hand, such functionality may have less of a beneficial impact on accidents or potential accidents in desert locations, such as Nevada or New Mexico.
  • Construction-related data may also be collected and analyzed. Construction-related accident avoidance and/or mitigation technology, functionality, systems, or associated equipment may be more pertinent in large urban areas involving significant and lengthy construction or road connector projects that may include frequently changing travel patterns with little notice to drivers.
  • the data gathered may relate to autonomous vehicle telematics variables. From which, usage-based insurance policies, premiums, rates, discounts, rewards, and/or other insurance-related items may be estimated, as discussed elsewhere herein.
  • an updated or adjusted insurance policy, premium, rate, and/or discount may be estimated for the driver, such as by a remote processor or server associated with the insurance provider.
  • a message may be wirelessly communicated to the vehicle or mobile device associated with the driver that indicates that they may save a given amount of money on their auto insurance if they increase usage of the automatic braking technology or functionality to a certain percentage of time, such as up to 90% of vehicle driving time for example. Usage of other technologies and functionalities (including the technologies and functionalities discussed elsewhere herein) may be monitored, and recommended usages thereof (and associated insurance savings) may be provided to the insured or driver for their review and/or approval.
  • a percentage of time that the vehicle is in a (1) “manual” mode or operation; (2) semi-automated, semi-automatic, or “semi-autonomous” mode or operation; and/or (3) fully automated, fully automatic, or fully “autonomous” mode or operation may be determined from vehicle sensor data that is remotely collected, such as at or by an insurance provider remote processor or server.
  • the insurance provider remote processor or server may determine auto insurance discounts increases or premium reductions based upon proposed changes to the time that the vehicle is operated in each mode, i.e., manual, semi-autonomous, or fully autonomous. For instance, driving in a semi-autonomous, or even autonomous mode, of operation may be the safest for a given technology or functionality and/or under certain driving conditions (e.g., freeway driving in clear weather and moderate traffic). The driver may be offered a reduced insurance premium or rate to increase usage of the semi-autonomous, or even autonomous, technology or functionality, and/or to increase usage of the semi-autonomous, or even autonomous, technology or functionality in certain driving conditions.
  • driving in a semi-autonomous, or even autonomous mode, of operation may be the safest for a given technology or functionality and/or under certain driving conditions (e.g., freeway driving in clear weather and moderate traffic).
  • the driver may be offered a reduced insurance premium or rate to increase usage of the semi-autonomous, or even autonomous, technology or functionality, and
  • the insurance provider may offer a plurality of separate tiers of auto insurance policies, premiums, rates, discounts, etc.
  • the insurance provider may offer three separate tiers.
  • the three separate insurance tiers of premiums, rates, discounts, etc. may be based upon (a) a manual insurance rate; (b) a semi-autonomous insurance rate; and/or (c) a fully autonomous insurance rate.
  • the manual insurance rate may be associated with manual operation of the vehicle; the semi-autonomous insurance rate may be associated with semi-autonomous operation of the vehicle; and/or the fully autonomous insurance rate may be associated with autonomous operation of the vehicle.
  • the data gathered may be used to provide feedback to the customer or insured. For instance, if the vehicle is presently traveling on the highway, a recommendation or offer may be presented to the driver, such as via wireless communication with the vehicle that indicates that if the driver places the vehicle into autonomous or semi-autonomous driving mode, the risk of collision may be reduced and/or the driver may be receive a discount, and/or lower premium on his or her auto insurance.
  • auto insurance policies, premiums, rates, discounts, rewards, and/or other insurance-related items may be adjusted, updated, or generated based upon (1) the autonomous or semi-autonomous technology and/or functionality; (2) an amount or percentage of driver usage of that technology and/or functionality; and/or (3) driver characteristics.
  • the driver characteristics that may be taken into consideration include driver age, driver health, and/or past driving or accident history.
  • the data gathered may originate from various smart parts and/or pieces of smart equipment mounted on a vehicle, including parts configured for wired or wireless communication.
  • a vehicle may be equipped with smart brakes; smart tail, head, or turn lights; smart tires; etc.
  • Each piece of smart equipment may have a wired or wireless transmitter.
  • Each piece of smart equipment may be configured to monitor its operation, and/or indicate or communicate a warning to the driver when it is not operating properly.
  • a rear brake light when a rear brake light is out, such as from faulty repair or from normal burn out, that fact may be detected by smart vehicle functionality and the driver may be promptly notified. As a result, the driver may be able to repair the faulty brake light before an accident caused by the faulty brake light occurs.
  • the data gathered may also indicate window wipers are not operating properly, and need to be replaced.
  • the insurance provider may adjust or update insurance policies, premiums, rates, discounts, and/or other insurance-related items based upon the smart equipment warning functionality that may alert drivers of vehicle equipment or vehicle safety equipment (lights, brakes, etc.) that need to be replaced or repaired, and thus may reduce collision risk.
  • the technology may also reduce risk of theft. For instance, stolen vehicles may be tracked via on-board GPS units and wireless transmitters. Also, the breaking and entering, and/or hot wiring, of vehicles may be more difficult through the use of anti-hacking measures for smart vehicles or vehicles with electrical or electronic control systems.
  • the insurance provider may adjust insurance premiums, rates, and/or other insurance-related items based upon the reduced risk of theft.
  • the present embodiments may relate to the assignment of fault.
  • the assignment of fault may be based upon sensor data and/or other data gathered or collected from, or by, the vehicle.
  • the assignment of fault may impact the future rating for one or more drivers, and/or one or more vehicles equipped with one or more autonomous or semi-autonomous technologies, functionalities, systems, and/or pieces of equipment.
  • the assignment of fault determination from sensor and/or vehicle data may relate to, and/or involve, determining who was in control of, or driving, the vehicle at the time of the accident (such as either the human driver or the vehicle itself), and/or determining who was at fault or liable for the collision or accident—the human driver or the vehicle. For instance, did the vehicle give the driver enough time (e.g., half a second) to take manual control of the vehicle before the time of impact, or was the driver not attentive enough before an accident.
  • the assignment of fault may include a determination of who pays the claim associated with a vehicle accident and/or determine future ratings for certain types of technology or functionality, and/or certain insurance policy holders.
  • Fault for a vehicle collision or accident may be partially or fully assigned to one or more drivers, and/or one or more vehicles equipped with one or more autonomous or semi-autonomous technologies, functionalities, systems, and/or pieces of equipment.
  • insurance coverage may provide for immediate coverage/payment to an insured in the case of an accident. After which, based upon data collected from the smart vehicle or sensors, blame or fault may be assigned for the accident, such as to either the driver or the autonomous or semi-autonomous technology or functionality, and/or to another driver, or autonomous or semi-autonomous vehicle involved in the accident.
  • the data gathered may help determine who was in control of the vehicle before, during, and/or after a vehicle collision or accident.
  • a human driver, or an autonomous or semi-autonomous vehicle may have been in control of the vehicle at the time of accident.
  • the data may be used to identify whether there was enough time for a driver to takeover manually. For instance, once a hazardous condition is identified (e.g., vehicles slowing down abruptly or heavy congestion ahead, or vehicle accident ahead), did a semi-autonomous technology function correctly, and/or did the human driver have the time to take manual control of the vehicle and avoid a collision or accident.
  • a hazardous condition e.g., vehicles slowing down abruptly or heavy congestion ahead, or vehicle accident ahead
  • did a semi-autonomous technology function correctly did the human driver have the time to take manual control of the vehicle and avoid a collision or accident.
  • a performance of the artificial intelligence or computer instructions associated with the autonomous vehicle, technology, or functionality may be evaluated.
  • evaluating the performance of the autonomous technology or functionality may determine fault—such as which autonomous vehicle was at fault (for an accident involving two autonomous vehicles) or whether an autonomous vehicle or a human driver was at fault (for an accident involving two vehicles, one driven by a human driver and one driven by autonomous vehicle technology, functionality, or systems that may include associated artificial intelligence and/or processors).
  • Autonomous or semi-autonomous technology, functionality, and/or system usage data may be used to identify and present a driver one or more potential premium or rate reductions with increased usage of the technology, functionality, and/or system.
  • a number of “what if” insurance-related scenarios may be calculated and then presented to a driver and/or insured for their review, approval, and/or modification. The different scenarios may be presented to a driver on their mobile device or a smart vehicle display screen, or other in dash display.
  • Autonomous or semi-autonomous vehicle technology or functionality may relate to vehicle parking.
  • the technology or functionality may determine an available parking spot in an urban area or large city.
  • the smart vehicle may make recommendations to the driver regarding the best available parking spot remotely identified. For instance, the best available parking spot may be determined based upon the cost of the parking; safety of the parking spot, lot, or garage; the risk of theft or other liability associated with the parking spot, lot, or garage; and/or other factors.
  • the recommendation may be generated by an insurance provider remote processor or server.
  • the recommendation with respect to best available parking spot may include information regarding an adjustment to the driver's present insurance policy, premium, rate, and/or discount based upon the driver accepting the recommendation and parking the vehicle in the best available parking spot.
  • a discount and/or lower premium may be offered to the driver to encourage safer parking habits that may reduce the risk of vehicle damage or theft.
  • additional insurance coverage may be offered to the driver. For instance, if the vehicle is going to be parked on a busy street and overnight, it may have a higher risk of damage or theft.
  • a remote processor or server associated with the insurance provider may estimate and/or offer an appropriate increase in auto insurance coverage to the insured or driver, such as via wireless communication with a smart vehicle controller or a mobile device of the insured or driver.
  • the system 100 may operate to monitor use of autonomous (and/or semi-autonomous) operation features and present feedback to vehicle operators. This may occur in real time as operating conditions change or may occur on a periodic basis in response to vehicle use and environmental conditions.
  • the use of autonomous operation features may be assessed to determine whether changes to the number, type, configuration, or settings of the autonomous operation features used may reduce the risk associated with vehicle operation under various conditions. Presenting or otherwise providing the information to the vehicle operator may improve the effective use of the autonomous operation features and/or reduce the risks associated with vehicle operation.
  • the vehicle operator may be able to maximize the effectiveness of the autonomous operation feature, maximize vehicle insurance coverage, and/or minimize vehicle insurance expense.
  • FIG. 11 illustrates an exemplary autonomous (and/or semi-autonomous) operation feature monitoring and feedback method 1100 .
  • the method 1100 may be performed by the controller 204 or the server 140 at any time while the vehicle 108 is in operation.
  • the method 1100 may be implemented only when the vehicle 108 is stationary, when the autonomous (and/or semi-autonomous) operation features are controlling the vehicle 108 , when the controller 204 or server 140 determines that the conditions meet certain criteria (e.g., when the vehicle is more than a predetermined distance from environmental obstacles on a restricted access highway, etc.), and/or when the vehicle 108 is first started, such that the method 1100 does not distract the vehicle operator.
  • certain criteria e.g., when the vehicle is more than a predetermined distance from environmental obstacles on a restricted access highway, etc.
  • the controller 204 may determine actual use levels of the autonomous operation features at block 1102 . This may include current use of the features and/or past use of the features, either generally or under similar conditions. In some embodiments, the determination of use levels may include a determination of the use of versions, configurations, or settings related to the autonomous operation features.
  • the controller 204 may receive sensor data from the sensors 120 , as discussed above.
  • the received sensor data may include information regarding the vehicle 108 , the vehicle's environment (e.g., traffic conditions, weather conditions, etc.), and/or the vehicle operator.
  • the sensor data may include information regarding the physical or mental state of the vehicle operator using sensors 120 disposed within the vehicle 108 or communicatively connected thereto (e.g., disposed within or communicatively connected to a mobile device 110 , such as a smart phone, and/or a wearable computing device, such as a smart watch or smart glasses).
  • This sensor data may include data from interior cameras, microphones, accelerometers, and/or physiological sensors (e.g., thermometer, microphone, thermal image capture device, electroencephalograph, galvanic skin response sensor, heart rate sensors, respiratory rate sensor, other biometric sensors, etc.).
  • physiological sensors e.g., thermometer, microphone, thermal image capture device, electroencephalograph, galvanic skin response sensor, heart rate sensors, respiratory rate sensor, other biometric sensors, etc.
  • the received sensor data may exclude sensor data regarding the vehicle operator or the physical or mental state of the vehicle operator.
  • the controller 204 or the server 140 may receive communication data from external sources.
  • the communication data may include direct communication data from other autonomous vehicles, communicating infrastructure, and/or other smart devices (e.g., mobile devices carried or worn by pedestrians, passengers in other vehicles, etc.).
  • the communication data may also include indirect communication data received by the controller 204 or the server 140 via the network 130 (e.g., information regarding traffic, construction, accidents, weather, local time, local events, local traffic patterns, local accident statistics, general accident statistics, etc.).
  • the indirect information may be obtained from database 146 or from other networked or third-party databases.
  • indirect communication data may be obtained regarding the risk level of autonomous operation features relative to manual vehicle operation on major highways during typical commuting times in urban areas in light rain, which may be combined with information from a weather service indicating light rain and information from a map service indicating the vehicle 108 is on a major highway (using GPS data from the sensors 120 ).
  • traffic databases could be accessed to receive information regarding accidents and/or construction further ahead along a route.
  • the server 140 or the controller 204 may determine an optimal use level for the autonomous operation features available within the vehicle 108 and/or a suggestion regarding the optimal autonomous operation feature use level under the conditions.
  • the optimal use level or suggestion may include the types and versions of autonomous operation features to use, configurations of the features, and/or settings relating to the features.
  • the server 140 or the controller 204 may determine one or more optimal use levels for the autonomous operation features based upon the sensor data and communication data received in blocks 1104 and 1106 using any known or later-developed optimization techniques.
  • the risk levels associated with each combination of use levels for autonomous operation features may be determined and stored in one or more databases, such that the server 140 or controller 204 may access and compare the appropriate database entries to determine the optimal use levels.
  • one or more optimal use levels may be determined and stored in one or more databases, such that the server 140 or controller 204 may determine the optimal use level by accessing the database entry corresponding to the sensor data and communication data.
  • the server 140 or controller 204 may determine optimal use levels by determining risk levels for a variety of combinations of configurations and settings associated with autonomous operation features based on the received sensor data and communication data.
  • the combination or combinations determined to have the lowest risk may be determined to be the optimal feature use levels.
  • the determination of optimal feature use may be based upon the received sensor data and/or the communication data.
  • the received sensor data and/or communication data may include information regarding the physical, mental, and/or emotional state of the vehicle operator, as noted above.
  • the determination of the optimal feature use level may either include or exclude information regarding the state of the vehicle operator from the determination.
  • the determination may be based in part upon the previous driving history of a vehicle operator, which may indicate that the vehicle operator has an increased risk of an accident in low light environments.
  • the determination may compare the expected performance of the various autonomous operation features against the expected performance of the vehicle operator, which may cause the server 140 or controller 204 to determine an optimal feature use level that includes more autonomous operation feature use than would otherwise be determined to be optimal.
  • the server 140 or the controller 204 may not determine the optimal use level based upon the previous driving history of the vehicle operator from the previous example, which may result in a determination of an optimal feature use level that includes less use of autonomous operation features than in the preceding example.
  • the determined optimal use level may be used to further determine an autonomous (and/or semi-autonomous) operation feature use suggestion.
  • the use suggestion may include one or more settings relating to autonomous operation features, enabling or disabling particular autonomous operation features, using specific versions of autonomous operation features, resuming manual operation of the vehicle, temporarily ceasing autonomous and/or manual operation of the vehicle, and/or similar changes to the use and configuration of the autonomous operation features in operating the vehicle 108 .
  • the determined use suggestion may include changes to the use of autonomous operation features, use of additional autonomous operation features, and/or use of fewer autonomous operation features.
  • the suggested optimal use levels of autonomous (and/or semi-autonomous) operation features determined at block 1108 is compared against the actual autonomous operation feature use levels determined at block 1102 .
  • the server 140 or the controller 204 causes a suggestion of autonomous operation feature use to be presented to the vehicle operator at block 1112 .
  • the suggestion may not be presented when the difference between the optimal use level and the actual use level is below a predetermined threshold.
  • the server 140 may determine not to present the suggested autonomous operation use to the vehicle operator where the difference would only result in a risk reduction equivalent to a monetary value below five cents.
  • the suggestion presented at block 1112 may be presented using a display within the vehicle 108 , a mobile device 110 , or other means, including visual and/or audible notifications.
  • the suggestion may include a recommendation that the vehicle operator enable or use one or more additional autonomous (and/or semi-autonomous) operation feature, that the vehicle operator change the settings or configuration for one or more autonomous (and/or semi-autonomous) operation features, that the vehicle operator disable or discontinue use of one or more autonomous (and/or semi-autonomous) operation features, and/or related changes that may be made to the use of the autonomous (and/or semi-autonomous) operation features.
  • the suggestion may further include one or more reasons for making a change to the autonomous operation feature use, such as an indication of a reduction in risk, a percentage reduction in the probability of a collision, an increase in a probability of completing the trip without incident, a reduction in a premium or other policy charge, a reduction in a rate, an increase in a coverage amount, an increase in a coverage type, a reduction in a deductible, and/or related information to induce the vehicle operator to change the autonomous operation feature use.
  • a suggestion presented to the vehicle operator may indicate that updating to a newer software version of an autonomous operation feature would result in a decrease of a certain amount in a vehicle insurance premium.
  • the vehicle operator may make a selection upon presentation of the suggestion, which selection may cause the use levels of one or more of the autonomous operation features to be adjusted (e.g., to match the one or more optimal use levels). In other embodiments, the vehicle operator may otherwise adjust or control the use levels, as discussed above. A change or adjustment to the use, configuration, or settings of the autonomous operation features may further cause a change or adjustment to costs or coverage associated with a vehicle insurance policy, as discussed above.
  • the server 140 or the controller 204 determine whether vehicle operation is ongoing at block 1114 .
  • the method 1100 may repeat the steps of blocks 1102 - 1112 .
  • the method 1100 may repeat only when a predetermine period of time (e.g., 5 minutes, 15 minutes) has passed, when vehicle operating conditions have sufficiently changed (e.g., upon exiting a highway, entering fog, sunset, etc.), and/or when a sufficient change in the recommendation has occurred (e.g., risk level, monetary incentive, feature use level recommendation, etc.).
  • the method 1100 may terminate. In some embodiments, however, the method 1100 may be implemented either before or after vehicle operation, in which case the actual autonomous (and/or semi-autonomous) operation feature use determined in block 1102 may be based upon the settings of the autonomous operation features that had been previously used, the settings that would be applied if the vehicle were to be used at that time, or the default settings.
  • some embodiments may determine elevated risk levels and present warnings to the vehicle operator. In some embodiments, this may include warnings regarding situations where no changes to the optimal autonomous operation feature use level would be suggested, but where an increased risk nonetheless exists. For example, communication data regarding recent snowfall may be combined with sensor data indicating a high frequency of slipping wheels to determine a high risk of an accident exists at the current speed on a snow-covered road. The vehicle operator might then respond by reducing the speed of the vehicle, resuming manual control of the vehicle, and/or selecting an alternate route using major thoroughfares that are clear of snow. Such responses may further cause an adjustment in a cost or coverage level associated with a vehicle insurance policy.
  • FIG. 12 illustrates an exemplary autonomous (and/or semi-autonomous) operation feature monitoring and alert method 1200 .
  • the method 1200 may be performed by the controller 204 or the server 140 at any time while the vehicle 108 is in operation.
  • the controller 204 or server 140 may determine the use of the autonomous operation features at block 1202 . This may include current use of versions, configurations, or settings related to the autonomous operation features.
  • the controller 204 or server 140 may further receive sensor data and communication data, respectively, at blocks 1204 and 1206 .
  • the sensor data may be received from sensors 120 disposed within the vehicle 108 , and the communication data may include information regarding the vehicle environment (including information regarding the rate of incidents in similar conditions or locations based on historical data).
  • This information may be used at block 1208 to determine the risk associated with operation of the vehicle under the conditions.
  • the sensor data, communication data, and the determination of risk may either include or exclude information regarding one or more vehicle operators (e.g., the physical, mental, and/or emotional state of the vehicle operator).
  • the server 140 or the controller 204 may determine a risk level associated with the operation of the vehicle under the current conditions. This may include a determination of the risk associated with the autonomous (and/or semi-autonomous) vehicle operation features then in use, or it may include a determination of the risk associated with various configurations or settings of autonomous operation features as discussed above with respect to method 1100 . In some embodiments, the determination may not include information regarding one or more vehicle operators.
  • the server 140 or controller 204 may determine one total risk level or a plurality of risk levels associated with vehicle operation at block 1208 . For example, separate risk levels may be determined for different types of potential incidents (e.g., collisions with other vehicles, loss of control or traction, collisions with pedestrians, collisions with stationary obstructions, etc.).
  • the server 140 or the controller 204 may compare the determined risk level against a warning threshold risk level.
  • the difference between the determined risk level and a risk level associated with an optimal autonomous (and/or semi-autonomous) operation feature use level may be compared against the warning threshold, and the warning threshold may be set at a level such that a warning is triggered only when the additional risk from suboptimal autonomous (and/or semi-autonomous) operation feature use, configuration, and/or settings exceeds a certain level.
  • the risk level may be compared against a plurality of predetermined warning thresholds, and the warning presented to the vehicle operator may be determined based upon the highest warning threshold exceeded by the risk level.
  • the controller 204 or server 140 may cause a warning to be presented to the vehicle operator at block 1212 .
  • the warning presented at block 1212 may be presented using a display within the vehicle 108 , a mobile device 110 , or other means, including visual, audible, and/or haptic notifications.
  • the warning may specify one or more causes of the elevated risk (e.g., weather, speed, hardware malfunctions, etc.). Alternatively, the warning may simply alter the vehicle operator to an elevated risk level.
  • the vehicle operator may make a selection upon presentation of the alert, which selection may cause the use, configuration, or settings of one or more of the autonomous (and/or semi-autonomous) operation features to be adjusted (e.g., the vehicle operator may resume full control of operation, the vehicle operator may cede control of operation to the autonomous (and/or semi-autonomous) operation features, etc.).
  • the vehicle operator may otherwise adjust or control the use levels, as discussed above.
  • a change or adjustment to the use, configuration, or settings of the autonomous operation features may further cause a change or adjustment to costs or coverage associated with a vehicle insurance policy, as discussed above.
  • the server 140 or the controller 204 determine whether vehicle operation is ongoing at block 1214 .
  • the method 1200 may repeat the steps of blocks 1202 - 1212 .
  • the method 1200 may terminate.
  • the system 100 may be used to determine or allocate fault upon the occurrence of an accident or other collision involving the vehicle 108 .
  • Information regarding the operation of the vehicle 108 may be recorded and stored during operation, which may then be used to determine the cause of a collision or accident automatically upon receiving an indication of the occurrence of such.
  • Fault may be allocated to either the vehicle operator, one or more autonomous operation features, or a third party (e.g., another motorist or autonomous vehicle).
  • Such allocation of fault may be further used to adjust one or more of an insurance policy premium, a risk level, a rate category, a penalty, or a discount relating to a vehicle insurance policy.
  • the allocation of fault may also be used to determine whether to cancel an insurance policy, adjust a deductible, adjust a policy limit, and/or determine a payment associated with the collision or accident.
  • an autonomous operation feature is determined to be wholly or partially responsible for the accident, the risk levels or risk profile associated with that autonomous operation feature may be revised, such that the risk levels or risk profile of other autonomous vehicles using the feature may also be adjusted.
  • FIG. 13 illustrates an exemplary fault determination method 1300 for determining fault following an accident based upon sensor data and communication data.
  • the method 1300 may receive sensor data and communication data at block 1304 and may further receive information regarding the operation of one or more autonomous (and/or semi-autonomous) operation features at block 1306 .
  • this information may be used to make a preliminary determination of whether a third party is at fault at block 1308 , in which case there may be no fault allocated to the vehicle operator and/or autonomous (and/or semi-autonomous) operating features.
  • the method 1300 may then determine and allocate fault between the vehicle operator and one or more autonomous (and/or semi-autonomous) operation features in blocks 1314 - 1324 .
  • the determination of fault may further be used to determine and/or adjust a coverage level at block 1326 , such as a deductible level or a policy status.
  • the determination process of method 1300 may depend upon whether the vehicle 108 is operated in a fully autonomous, partially autonomous, or manual operation mode at the time of the accident.
  • the server 140 may determine and/or allocate fault without human involvement.
  • the server 140 may present relevant information and/or a determination of fault to a reviewer (e.g., a claims adjuster or other specialist) for verification or further analysis.
  • the presented information may include summaries or detailed reports of sensor data and/or communication data, including still images or video recordings from the sensors 120 within the vehicle 108 or other sensors at the location of the accident (e.g., sensors disposed within other vehicles involved in or near the accident site, sensors disposed within infrastructure elements, etc.).
  • the method 1300 may be implemented by the mobile device 110 , the on-board computer 114 , the server 140 , and/or some combination of these components.
  • the server 140 may receive an indication of an accident involving the vehicle 108 .
  • the server 140 or controller 204 may generate this indication automatically based on sensor data, or it may be initiated manually by a vehicle operator or another person following the accident. However the indication is received, it may cause the method 1300 to proceed to the one or more determinations of fault.
  • the server 140 may receive sensor data from the one or more sensors 120 within the vehicle 108 and/or communication data from the communication component 122 and/or the communication unit 220 .
  • the server 140 may receive additional information from external sources, including sensor data from other vehicles or infrastructure, communication information from other vehicles or infrastructure, and/or communication information from third-party sources. For example, additional information may be obtained from other autonomous vehicles involved in the accident or near the accident.
  • the server 140 may additionally receive control decision data regarding the control decisions generated by one or more of the autonomous operation features of the vehicle 108 .
  • the sensor and/or communication data may be stored in the database 146 or in the program memory 160 or 208 , and/or in the RAM 164 or 212 during ordinary operation of the vehicle 108 , from which the data may be retrieved or accessed by the server 140 . Additionally, or alternatively, the sensor and/or communication data may be stored in another memory or database communicatively connected to the network 130 . In some embodiments, a back-up of the sensor and/or communication data may be stored within a memory (not shown) that may be designed to withstand the forces and temperatures frequently associated with a vehicular collision.
  • the server 140 may further receive information regarding the operation of the autonomous (and/or semi-autonomous) operation features in the vehicle 108 .
  • This information may include information regarding use, configuration, and settings of the features concurrent with the accident.
  • the information may further include information regarding control signals or outputs from the autonomous operation features to control the vehicle 108 . This may be useful, for example, in determining whether the autonomous operation feature failed to take appropriate control actions or whether the control signals were not implemented or were ineffective in controlling the vehicle 108 (e.g., such as may occur when on ice or when a defect prevents an electromechanical control from properly functioning).
  • autonomous operation feature data may be available for additional vehicles involved in the accident, which may be accessed or obtained by the server 140 . As above, the autonomous operation feature data may be recorded during ordinary operation of the vehicle 108 and accessed or obtained by the server 140 upon receipt of the indication of the accident.
  • the server 140 may determine whether a third party is at fault for the accident based upon the sensor data, communication data, and/or autonomous (and/or semi-autonomous) operation feature data received in blocks 1304 and 1306 .
  • Determining fault may generally include determining one or more of the following: a point of impact on the vehicle 108 , a point of impact on one or more additional vehicles, a velocity of the vehicle 108 , a velocity of one or more additional vehicles, a movement of the vehicle 108 , a movement of one or more additional vehicles, a location of one or more obstructions, a movement of one or more obstructions, a location of one or more pedestrians, a movement of one or more pedestrians, a measure of road surface integrity, a measure of road surface friction, a location of one or more traffic signs or signals (e.g., yield signs, stop signs, traffic lights, etc.), an indication of the state of one or more traffic signs or signals, a control signal generated by one or more autonomous operation features of the vehicle 108
  • the server 140 may include unavoidable accidents as being the fault of a third party (e.g., a bridge collapse, an animal suddenly darting into the path of a vehicle, etc.). Additionally, or alternatively, physical defects in the autonomous vehicle 108 or the physical components of the autonomous operation features (e.g., the sensors 120 , the on-board computer 114 , or connections within the vehicle 108 ) may be determined by the server 140 as being the fault of a third party (e.g., the vehicle maker, the original equipment manufacturer, or the installer).
  • a third party e.g., the vehicle maker, the original equipment manufacturer, or the installer
  • the server 140 may then determine whether the vehicle 108 or the vehicle operator had a chance to avoid the accident that was not taken at block 1312 . For example, the vehicle operator may have been able to avoid a collision by braking or swerving but for inattentiveness at the time of the accident. Where no such chance for the vehicle operator or the autonomous operation features to avoid the accident is determined to have existed at block 1312 , the fault determination method 1300 may terminate. Where such a chance to avoid the accident is determined to have existed at block 1312 , the method 1300 may continue to allocate a portion of the fault between the vehicle operator and the autonomous operation features.
  • the server 140 may determine the operating control status of the vehicle 108 at the time of the accident based upon the received autonomous (and/or semi-autonomous) operation feature data regarding the use, configuration, and settings of the features.
  • the vehicle 108 may be determined to have been either manually, fully autonomously, or partially autonomously operated at the time of the accident. Based upon the determination, the allocation of fault will be determined differently. Of course, any allocation of fault to a third party above at block 1310 may decrease the total fault to be allocated between the vehicle operator and the one or more autonomous operation features.
  • the fault may be allocated entirely to the vehicle operator.
  • the server 140 may adjust (or cause to be adjusted) the risk or rate profile associated with the vehicle operator at block 1322 in a manner similar to the adjustment that is typically made when a vehicle operator of a non-autonomous vehicle is determined to be at fault for an accident.
  • the fault will usually be assigned entirely to one or more autonomous operation features.
  • the autonomous operation feature may recognize a situation where autonomous operation is no longer feasible due to conditions in the vehicle's environment (e.g., fog, manual traffic direction, etc.).
  • the server 140 may determine whether the one or more autonomous operation features attempted to return control of the vehicle to the vehicle operator prior to the accident at block 1318 . Because such attempts may require the vehicle operator to be alert and capable of receiving control from the autonomous operation features, an adequate period of time for transition may be required.
  • the server 140 may allocate fault for the accident to the one or more autonomous operation features and adjust the risk levels and/or risk profiles associated with the one or more autonomous operation features at block 1324 .
  • the server 140 may allocate fault to the vehicle operator, and the vehicle operator's risk or rate profile may be adjusted at block 1322 .
  • the server 140 may allocate some portion of the fault to each of the vehicle operator and the autonomous operation features where an attempt to return control of the vehicle 108 to the vehicle operator was made, notwithstanding vehicle operator inattention.
  • the server 140 determines an allocation of fault between the vehicle operator and one or more autonomous operation features at block 1316 . This determination may include determining which autonomous operation features were in use at the time of the accident, the settings of those autonomous operation features, and whether the vehicle operator overrode the operation of the autonomous operation features. For example, the server 140 may determine that an autonomous operation feature such as adaptive cruise control without lane centering to be fully or primarily responsible for an accident caused by the vehicle 108 striking another vehicle directly ahead in the same lane.
  • an autonomous operation feature such as adaptive cruise control without lane centering to be fully or primarily responsible for an accident caused by the vehicle 108 striking another vehicle directly ahead in the same lane.
  • the server 140 may determine the vehicle operator to be fully or primarily at fault when the same adaptive cruise control without lane centering was engaged when the vehicle 108 struck another vehicle in an adjacent lane. Upon determining the allocation of fault at block 1316 , the server 140 may adjust the vehicle operator and/or autonomous operation feature risk levels accordingly in blocks 1322 and/or 1324 , respectively. In some embodiments, the use of autonomous operation features may be considered in reducing the adjustment to the vehicle operator risk or rate profile, thereby mediating the impact of the accident on the rates or premiums associated with vehicle insurance for the vehicle operator.
  • the method 1300 may further utilize the fault determinations for underwriting and/or claim administration, in addition to or as an alternative to adjusting one or more risk levels associated with the vehicle operator or the autonomous operation features.
  • the server 140 may further determine and/or adjust one or more coverage levels associated with an insurance policy covering the vehicle 108 based upon the allocation of fault between the vehicle operator and the autonomous operation features. For example, coverage levels may increase or decrease as the portion of the fault allocated to the autonomous operation feature increases or decreases. In some embodiments, the coverage level may be associated with the accident, such as a deductible, an estimate of a cost to repair or replace the vehicle, an estimate of a cost to repair or replace other property, and/or other payments or adjustments to payments associated with damage or injuries arising from the specific accident.
  • the coverage level may be associated with a general aspect of the insurance policy, such as a type of coverage, a maximum payment with respect to one or more types of coverage, a maximum payment per person, and/or a maximum total payment per accident.
  • a general aspect of the insurance policy such as a type of coverage, a maximum payment with respect to one or more types of coverage, a maximum payment per person, and/or a maximum total payment per accident.
  • a deductible associated with the insurance policy may be reduced or eliminated with respect to the accident.
  • the coverage level may be determined or adjusted based upon the portion of the fault allocated to each, either directly proportionally or otherwise.
  • the deductible for an accident caused in equal part by the vehicle operator and one or more autonomous operation features may be reduced by some amount (e.g., 25%, 50%, 75%).
  • some or all of the maximum coverage limits associated with the insurance policy may be increased (e.g., 25% increase in liability coverage, 50% increase in collision coverage, etc.).
  • the determination and/or adjustment of the coverage levels may include a determination of ongoing coverage levels or coverage status.
  • the server 140 may determine whether to cancel or decline to renew a policy based upon the allocation of fault and/or received information regarding the accident. For example, when the vehicle operator is determined to be wholly at fault or to be above a fault proportion threshold (e.g., the vehicle operator is responsible for 80% of the fault allocated), the server 140 may determine to cancel the policy in accordance with its terms. Alternatively, the server 140 may determine to require or exclude certain types or levels of coverage, either generally or in a manner dependent upon the settings or configuration of one or more of the autonomous operation features.
  • the server 140 may limit renewal options to exclude collision coverage or limit the maximum policy limit of collision coverage.
  • coverage may further be limited or cancelled based upon the use of specific autonomous operation features, such as where an autonomous operation feature is determined to be wholly or partially at fault. Determinations regarding coverage levels or coverage status may be further based upon information regarding the vehicle and/or the vehicle operator, such as information regarding previous accidents and/or the vehicle operator's history of use of autonomous operation features (including settings used). Further embodiments may require or exclude coverage (or levels of coverage) based upon future use of autonomous operation features.
  • the server 140 may determine to cancel coverage for future claims arising from manual operation of the vehicle by the vehicle operator at night.
  • the coverage may only extend to operation of the vehicle during daylight hours or autonomous operation of the vehicle at night.
  • the server 140 may cause one or more of the adjustments to the insurance policy to be presented to the vehicle operator or other customer.
  • the customer may be presented with one or more options relating to adjustments to the policy (e.g., options to either agree to use autonomous operation features with certain settings or to forego a type of coverage).
  • the server 140 may cancel or decline to renew the policy.
  • determinations of adjustments or cancellation may be presented to a reviewer (e.g., a claims adjuster or other specialist) for verification or further analysis prior to becoming effective.
  • the fault determination method 1300 may terminate.
  • the adjusted risk levels or profiles may be used to adjust a premium, surcharge, penalty, rate, or other cost associated with a vehicle insurance policy for the vehicle 108 and/or the vehicle operator.
  • the fault determination method 1300 may be implemented after payment has been made on claims relating to the accident. Because the sensor, communication, and autonomous operation feature data may be stored for later use, as discussed above, payment may be made shortly after occurrence of the accident. Determination of fault may then be made or verified at a later date. For example, operating data concerning an accident may be stored for later use following the accident, but payment of claims based upon a vehicle insurance policy covering the vehicle may be made before a determination of fault. Alternatively, or additionally, the fault determination method 1300 may be used to preliminarily determine fault immediately or shortly after the occurrence of an accident, and payment of claims may be made based upon such preliminary determination. Review and assessment of the preliminary determination may be completed at a later time, thereby allowing faster processing of claims.
  • vehicle may refer to any of a number of motorized transportation devices.
  • a vehicle may be a car, truck, bus, train, boat, plane, motorcycle, snowmobile, other personal transport devices, etc.
  • an “autonomous operation feature” of a vehicle means a hardware or software component or system operating within the vehicle to control an aspect of vehicle operation without direct input from a vehicle operator once the autonomous operation feature is enabled or engaged.
  • Autonomous operation features may include semi-autonomous operation features configured to control a part of the operation of the vehicle while the vehicle operator control other aspects of the operation of the vehicle.
  • autonomous vehicle means a vehicle including at least one autonomous operation feature, including semi-autonomous vehicles.
  • a “fully autonomous vehicle” means a vehicle with one or more autonomous operation features capable of operating the vehicle in the absence of or without operating input from a vehicle operator. Operating input from a vehicle operator excludes selection of a destination or selection of settings relating to the one or more autonomous operation features.
  • insurance policy or “vehicle insurance policy,” as used herein, generally refers to a contract between an insurer and an insured. In exchange for payments from the insured, the insurer pays for damages to the insured which are caused by covered perils, acts, or events as specified by the language of the insurance policy. The payments from the insured are generally referred to as “premiums,” and typically are paid by or on behalf of the insured upon purchase of the insurance policy or over time at periodic intervals.
  • premiums typically are paid by or on behalf of the insured upon purchase of the insurance policy or over time at periodic intervals.
  • insurance policy premiums are typically associated with an insurance policy covering a specified period of time, they may likewise be associated with other measures of a duration of an insurance policy, such as a specified distance traveled or a specified number of trips.
  • the amount of the damages payment is generally referred to as a “coverage amount” or a “face amount” of the insurance policy.
  • An insurance policy may remain (or have a status or state of) “in-force” while premium payments are made during the term or length of coverage of the policy as indicated in the policy.
  • An insurance policy may “lapse” (or have a status or state of “lapsed”), for example, when the parameters of the insurance policy have expired, when premium payments are not being paid, when a cash value of a policy falls below an amount specified in the policy, or if the insured or the insurer cancels the policy.
  • insurer insuring party
  • insurance provider are used interchangeably herein to generally refer to a party or entity (e.g., a business or other organizational entity) that provides insurance products, e.g., by offering and issuing insurance policies.
  • an insurance provider may be an insurance company.
  • insured insured party
  • polyicyholder policyholder
  • customer are used interchangeably herein to refer to a person, party, or entity (e.g., a business or other organizational entity) that is covered by the insurance policy, e.g., whose insured article or entity is covered by the policy.
  • a person or customer (or an agent of the person or customer) of an insurance provider fills out an application for an insurance policy.
  • the data for an application may be automatically determined or already associated with a potential customer.
  • the application may undergo underwriting to assess the eligibility of the party and/or desired insured article or entity to be covered by the insurance policy, and, in some cases, to determine any specific terms or conditions that are to be associated with the insurance policy, e.g., amount of the premium, riders or exclusions, waivers, and the like.
  • the insurance policy may be in-force, (i.e., the policyholder is enrolled).
  • an insurance provider may offer or provide one or more different types of insurance policies.
  • Other types of insurance policies may include, for example, commercial automobile insurance, inland marine and mobile property insurance, ocean marine insurance, boat insurance, motorcycle insurance, farm vehicle insurance, aircraft or aviation insurance, and other types of insurance products.
  • the present embodiments may provide a system and method for estimating the effectiveness of one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment on reducing a likelihood, and/or severity, of a vehicle accident, such as depicted in FIG. 14 (discussed further below).
  • the system and method for each autonomous or semi-autonomous vehicle technology or functionality that is analyzed, may evaluate or utilize the effect or impact of one or more accident-related factors or elements on the effectiveness of the respective autonomous or semi-autonomous vehicle technology or functionality.
  • the analysis or evaluation may determine the impact of each factor or element on how well an autonomous or semi-autonomous vehicle technology or functionality actually performs under certain conditions (such as driving, vehicle, and/or road conditions; accident or vehicle type; and/or other factors).
  • a system and method may analyze and/or evaluate the effectiveness of autonomous or semi-autonomous vehicle technology, functionality, systems, and/or equipment.
  • Individual technologies, functionalities, systems, and/or pieces of equipment may be evaluated that are related to: (1) fully autonomous (or driverless) vehicles; (2) limited driver control; (3) automatic or automated steering, acceleration, and/or braking; (4) blind spot monitoring; (5) collision warning; (6) adaptive cruise control; (7) parking assistance; (8) driver acuity or alertness monitoring; (9) pedestrian detection; (10) software security for smart vehicles; (11) theft prevention; (12) artificial intelligence upgrades or updates; (13) GPS functionality; (14) vehicle-to-vehicle wireless communication; (15) vehicle-to-infrastructure one or two-way wireless communication; and/or other technology and functionality, including that discussed elsewhere herein.
  • Each technology or functionality, and/or the accident avoidance and/or mitigation effectiveness thereof may be analyzed individually and/or in combination with one or more other technologies or functionalities.
  • each technology or functionality may determine an impact of one or more factors or elements that may degrade the performance of each autonomous or semi-autonomous vehicle technology or functionality. For instance, each factor or element may lower or limit the effectiveness of an autonomous or semi-autonomous vehicle technology or functionality with respect to accident avoidance and/or limiting the severity of vehicle accidents.
  • the factors or elements may be analyzed individually and/or in combination with one or more other factors or elements.
  • accident-related or other factors, elements, and/or conditions may impact the effectiveness of an autonomous or semi-autonomous vehicle technology or functionality.
  • the factors, elements, and/or conditions that may be evaluated may include: (1) point of vehicle impact during a vehicle accident; (2) type of road that an accident occurs on; (3) time of day that an accident occurs at; (4) weather conditions associated with an accident; (5) type of trip during which the accident occurred (short, long, etc.); (6) vehicle style for the vehicle(s) involved in an accident; (7) whether the vehicles involved in the accident were equipped with vehicle-to-vehicle wireless communication functionality; (8) whether the vehicle(s) involved in the accident were equipped with vehicle-to-infrastructure or infrastructure-to-vehicle wireless communication functionality; and/or other factors, elements, and/or conditions associated with, or impacting, individual vehicle accidents.
  • An evaluation of the foregoing factors, elements, and/or conditions with respect to multiple vehicle accidents involving vehicles having one or more autonomous or semi-autonomous vehicle technologies or functionalities may indicate or suggest: (a) an overall effectiveness for each individual autonomous or semi-autonomous vehicle technology or functionality, and/or (b) the impact (whether negative or positive) of each factor or element (type of road, type of vehicle; time of day; weather conditions, type of vehicle crash, i.e., point of impact; etc.) on the effectiveness of each autonomous or semi-autonomous vehicle technology, functionality, and/or associated equipment.
  • insurance premiums, rates, discounts, rewards, points, and/or other insurance-related items for vehicles having one or more autonomous or semi-autonomous vehicle technologies or functionalities may be generated, adjusted, and/or updated.
  • Characteristics and/or driving behaviors of individual drivers or customers may also be used to estimate, generate, and/or adjust insurance premiums, rates, discounts, rewards, and/or other insurance-related items for vehicles having one or more autonomous or semi-autonomous vehicle technologies or functionalities.
  • Driver characteristics and/or driver behavior, as well as driver location or home address, may be compared, or analyzed in conjunction, with the factors or elements that may impact the accident avoidance or mitigation effectiveness of each autonomous or semi-autonomous vehicle technology or functionality.
  • a driver or insured may mainly drive on the highway, during daylight hours, and/or primarily for short commutes to and from work.
  • the driver or insured's vehicle may have certain autonomous or semi-autonomous vehicle technologies or functionalities that have been established to decrease the likelihood of an accident, the severity of any accident, and/or otherwise increase safety or vehicle performance during highway, daylight, and/or short commute driving. If so, the insurance rate, premium, discount, and/or another insurance-related item for the driver or insured may be adjusted in accordance with the estimated lower risk (of accident, and/or severe accident).
  • the impact of one factor (point of vehicle impact) on the effectiveness of accident avoidance and/or mitigation for an autonomous or semi-autonomous vehicle technology or functionality may be determined.
  • the impact of head-on collisions on the accident avoidance and/or mitigation effectiveness of automatic braking and/or automatic steering functionality may be analyzed.
  • Also analyzed may be the effect of point of vehicle impact on the accident avoidance and/or mitigation effectiveness of automatic acceleration functionality.
  • the impact of point of vehicle impact on the accident avoidance and/or mitigation effectiveness of other autonomous or semi-autonomous technologies and/or functionalities, including those discussed elsewhere herein, may additionally or alternatively be evaluated.
  • the impact of another factor (vehicle size or type) on the effectiveness of accident avoidance and/or mitigation for an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment may be determined.
  • the impact of the vehicle being a compact car, mid-sized car, truck, SUV (sport utility vehicle), etc. on the accident avoidance and/or mitigation effectiveness for blind spot monitoring functionality and/or driver acuity monitoring functionality may be analyzed.
  • the impact of vehicle size or type on the accident avoidance and/or mitigation effectiveness of other autonomous or semi-autonomous technologies and/or functionalities, including those discussed elsewhere herein, may additionally or alternatively be evaluated.
  • the impact of another factor (type of road) on the effectiveness of accident avoidance and/or mitigation for an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment may be determined.
  • the impact of the type of road whether a freeway, highway, toll way, rural road or two-lane state or county highway, and/or downtown or city street
  • the amount of time or percentage of vehicle usage that an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment is used by the driver or vehicle operator may be determined from sensor or smart vehicle data.
  • Technology usage information gathered or collected may be used to generate, update, and/or adjust insurance policies, premiums, rates, discounts, rewards, points, programs, and/or other insurance-related items.
  • the methods and systems described herein may be viewed as combining information regarding autonomous (and/or semi-autonomous) vehicle operation technology with information regarding environmental or usage elements to evaluate one or more autonomous (and/or semi-autonomous) operation features, determine one or more risk factors for the autonomous (and/or semi-autonomous) operation features, and determine vehicle insurance premiums based upon the risk factors.
  • the autonomous operation features may include an autonomous driving software package or artificial intelligence for operating an automobile. Evaluation of the autonomous operation features may include evaluating both software and hardware associated with the features in a test environment, as well as evaluating actual loss experience associated with vehicles using the features in ordinary operation (i.e., operation not in a test environment).
  • the risk factors may be associated with the relative ability of the autonomous operation features to make control decisions that avoid accidents and other collisions.
  • the risk factors may be included in determining insurance policy premiums, which may in some embodiments include other factors relevant to the determination of the total risk associated with one or more types of insurance coverage for an autonomous vehicle.
  • FIG. 14 illustrates a high-level flow diagram of an exemplary autonomous (and/or semi-autonomous) automobile insurance pricing system.
  • Information regarding one or more autonomous operation feature technologies is collected, accessed, or otherwise received at block 1402 .
  • Such information may relate to one or more of the following technologies: a fully autonomous (driverless) vehicle operating technology, a limited driver control technology, an automatic steering technology, an automatic acceleration and/or braking technology, a blind spot monitoring and/or other information augmenting technology, a collision and/or other warning technology, an adaptive cruise control technology, a parking assist technology, and/or other autonomous operation technologies (including those described elsewhere herein or later developed).
  • the autonomous operation feature technologies of block 1402 may be associated with one or more environmental or usage elements, information regarding which may be collected, accessed, or otherwise received at block 1404 .
  • Such information may relate to one or more of the following elements: a point of impact between the autonomous automobile and another object (e.g., another vehicle, an infrastructure component, or another moving or fixed object within the autonomous automobile's environment), a type of road (e.g., a limited access highway, a residential neighborhood street, or a main thoroughfare), a time of day and/or date (e.g., rush hour, weekend, or holiday), a weather condition (e.g., light levels, cloud cover, precipitation, temperature, wind, or ground cover such as ice or snow), a type and/or purpose of vehicle trip (e.g., commuting, interstate travel, or leisure), a vehicle style and/or type, a vehicle-to-vehicle communication, or a vehicle-to-infrastructure communication.
  • the information regarding the technologies and elements may then be used in evaluating the performance of the autonomous (and/or semi-autonomous) operation features.
  • the performance or sophistication of the autonomous operating features e.g., autonomous driving software or artificial intelligence
  • the evaluation may include a variety of combinations of technologies and elements, and one or more risk levels or risk profiles may be determined as part of or based upon the evaluation.
  • the evaluation may include testing the autonomous operation features on a test track or other test facility by installing the features within a test automobile.
  • the test performance may then be supplemented or compared with actual loss experience information relating to the autonomous operating features in actual driving situations recorded at block 1408 .
  • the recorded actual loss experience from block 1408 and/or the evaluated test performance from block 1406 may be used to determine a relative or total risk factor for the autonomous operation features based upon the observed or expected ability of the autonomous operation features to make driving decisions for the autonomous automobile and avoid crashes, collisions, or other losses at block 1410 .
  • a relative or total risk factor for the autonomous operation features based upon the observed or expected ability of the autonomous operation features to make driving decisions for the autonomous automobile and avoid crashes, collisions, or other losses at block 1410 .
  • one or more premiums or components of premiums for an automobile insurance policy may be determined at block 1412 , as discussed above. These premiums make take into account the risks associated with autonomous operation features or combinations of features, as well as expected environmental or usage conditions, factors, or levels.
  • the premiums determined at block 1412 may then be presented to a customer or potential customer for review, selection, or acceptance and purchase.
  • a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided.
  • the method may include: (a) evaluating, via a processor, a vehicle accident avoidance and/or mitigation effectiveness of, and/or associated with, an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment under real-world driving conditions, the real-world driving conditions including one or more conditions that effect or impact the likelihood, and/or severity, of a vehicle accident or collision; (b) updating, adjusting, and/or generating an auto insurance policy, premium, rate, and/or discount, via the processor, based upon the accident avoidance and/or mitigation effectiveness of the autonomous or semi-autonomous vehicle technology, functionality, system, or equipment for a vehicle equipped with the autonomous or semi-autonomous vehicle feature, technology, system, and/or piece of equipment; (c) presenting (all or a portion of) the updated, adjusted, and/or generated auto insurance policy, premium, rate, and/or
  • the step of (a) evaluating, via the processor, an accident avoidance or mitigation effectiveness of, or associated with, an autonomous or semi-autonomous vehicle technology, functionality, system, or piece of equipment under real-world driving conditions may include: (i) analysis of a plurality of vehicle accidents involving one or more vehicles having the autonomous or semi-autonomous vehicle technology, functionality, system, or piece of equipment, and/or (ii) testing vehicles equipped with the autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment under real-world conditions and gathering data.
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided.
  • the method may include: (1) updating, adjusting, and/or generating an auto insurance policy, premium, rate, and/or discount, via a processor, based upon and/or taking into consideration: (a) one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment; (b) conditions and/or factors impacting the effectiveness of each autonomous or semi-autonomous vehicle feature, technology, system, and/or piece of equipment with respect to accident avoidance and/or mitigation; (c) driver or insured actual characteristics or driving behavior, and/or geographical location associated with the driver, insured, or vehicle; and/or (d) driver or insured actual usage of the one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment; (2) presenting on a display (such on a display of a computing device associated with the driver or insured, or a sales agent), all or a
  • the one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment may be or include an updated or revised version of computer or processing instructions related to the one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment.
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided.
  • the method may include: (i) estimating a risk factor associated with auto insurance, via a processor, based upon (1) one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment; and/or (2) one or more accident-related conditions or factors that impact the effectiveness of the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment, individually and/or as a group or collectively; (ii) generating, updating, and/or adjusting an auto insurance policy, premium, rate, and/or discount, via the processor, based upon the (a) risk factor associated with auto insurance estimated, and/or (b) actual driving characteristics and/or behaviors (such as typical driving patterns/paths/routes, geographical location, type of trips usually taken, etc.) of a driver, an insured, or an owner of a vehicle with having the one or more autonomous
  • An amount or percentage of driving time that the driver or insured uses the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, or pieces of equipment while driving the vehicle may be used to update, adjust, and/or generate the insurance policy, premium, rate, and/or discount.
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided.
  • the method may include: (1) applying an accident risk factor associated with one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment to an auto insurance policy, premium, rate, discount, reward, etc.
  • the risk factor being generated from evaluation of one or more driving and/or accident-related conditions impacting an effectiveness of the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment with respect to accident avoidance and/or mitigation; (2) presenting (under the direction and/or control of the processor) on a display of a computing device (such as a mobile device associated with the driver or insured, or a sales agent) (all and/or portions of) the auto insurance policy, premium, rate, discount, reward, etc.
  • a computing device such as a mobile device associated with the driver or insured, or a sales agent
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided.
  • the method may include (1) determining, via a processor, the automobile accident avoidance and/or mitigation related effectiveness associated with, or for, a revision or update of computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that may be stored on a non-transitory computer readable media or medium), the effectiveness determination taking into consideration: (a) actual vehicle accident information for accidents involving vehicles equipped with the revision or update of the computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment; and/or (b) physical testing of vehicles equipped with the revision or update of the computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment.
  • the method may also include (2) updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount for a vehicle equipped with the revision or update of computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment.
  • the method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided.
  • the method may include: (1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment; and/or (3) updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount for a vehicle equipped with the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment based upon the increase in accident avoidance or mitigation effectiveness determined.
  • the method may include additional, fewer,
  • a computer-implemented method of evaluating risk of autonomous or semi-autonomous vehicle technology may be provided.
  • the method may include (1) generating, via one or more processors, a virtual test scenario, the virtual test scenario including one or more accident-related factors or conditions; (2) applying, via the one or more processors, the virtual test scenario to a package of computer instructions that instruct a vehicle processor to perform an autonomous or semi-autonomous functionality; (3) analyzing, via the one or more processors, a performance of the autonomous or semi-autonomous functionality under virtual conditions associated with the virtual test scenario; (4) determining, via the one or more processors, an insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the package of computer instructions that instruct the vehicle processor to perform the autonomous or semi-autonomous functionality, and/or (5) generating, updating, or adjusting, via the one or more processors, a premium, rate, discount, reward, or other insurance item associated with an insurance policy for an autonomous or semi-autonomous vehicle employing
  • the one or more accident-related factors or conditions of the virtual test scenario may include road, construction, traffic, other vehicle, and/or weather factors or conditions.
  • the virtual test scenario may include a virtual simulation of virtual traffic traveling on a virtual road, and each virtual vehicle traveling on a virtual route at a virtual speed.
  • Determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the package of computer instructions may include determining whether the package of computer instructions made a correct or proper decision given the road, construction, traffic, other vehicle, and/or weather conditions of the virtual test scenario.
  • a computer-implemented method of evaluating risk of autonomous or semi-autonomous vehicle technology may be provided.
  • the method may include (1) collecting or receiving, via one or more processors, a virtual log of actual or real-world driving performance of autonomous or semi-autonomous functionality from a vehicle (or smart vehicle controller); (2) determining, via the one or more processors, an insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance of the autonomous or semi-autonomous functionality, respectively; and/or (3) generating, updating, or adjusting, via the one or more processors, a premium, rate, discount, reward, or other insurance item associated with an insurance policy for an autonomous or semi-autonomous vehicle employing the autonomous or semi-autonomous functionality based upon the insurance-based risk of, or associated with, the autonomous or semi-autonomous functionality.
  • the method may include additional, less, or alternate functionality, including that discussed elsewhere herein
  • determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance may include analysis of: (1) decisions that the autonomous or semi-autonomous functionality, or associated computer instructions, made; and/or (2) the environment and/or operating conditions (e.g., road, construction, traffic, and/or weather conditions) under which the decisions were made.
  • the environment and/or operating conditions e.g., road, construction, traffic, and/or weather conditions
  • determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance may include analysis of: (1) evasive maneuvers that the autonomous or semi-autonomous functionality, or associated computer instructions, made (or directed the vehicle to perform); and/or (2) the environment and/or operating conditions (e.g., road, construction, traffic, and/or weather conditions) under which the evasive maneuvers were made.
  • the environment and/or operating conditions e.g., road, construction, traffic, and/or weather conditions
  • determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance may include analysis of (1) how the autonomous or semi-autonomous functionality, or associated computer instructions, respond to other drivers on the road maneuvering or changing speed; and/or (2) grading, rating, or otherwise evaluating the responsiveness of the autonomous or semi-autonomous functionality, or associated computer instructions, to other drivers' driving behavior.
  • a computer-implemented method of evaluating risk of autonomous or semi-autonomous vehicle technology and/or adjusting autonomous or semi-autonomous vehicle technology may be provided.
  • the method may include (1) determining, via one or more processors, an optimum setting for an autonomous or semi-autonomous vehicle technology; (2) detecting, via the one or more processors, that an actual setting for an autonomous or semi-autonomous vehicle system of a vehicle is different than the optimum setting for the autonomous or semi-autonomous vehicle technology; (3) generating a recommendation, via the one or more processors, to change the actual setting for the autonomous or semi-autonomous vehicle system to the optimum setting; and/or (4) causing the recommendation, via the one or more processors, to be presented to a driver of the vehicle having the autonomous or semi-autonomous vehicle system to facilitate the driver changing the actual setting to the optimum setting (or otherwise accepting, or being notified of, an automatic change to the optimum setting).
  • the method may include additional, less, or alternate functionality, including that discussed elsewhere herein.
  • the method may include generating or adjusting, via the one or more processors, a premium, rate, discount, or reward of an auto insurance policy for the vehicle based upon the vehicle having or being equipped with the recommendation functionality associated with recommending optimum settings, and/or a percentage of the driver accepting the recommendations provided.
  • the optimum setting that is determined may be determined based upon a setting for the autonomous or semi-autonomous vehicle technology that reduces a likelihood of the vehicle employing the technology having, or being involved in, a vehicle accident or collision. Additionally or alternatively, the optimum setting that is determined may be determined based upon risk associated with the autonomous or semi-autonomous vehicle technology.
  • a computer-implemented method of determining accident fault may be provided.
  • the method may include (1) receiving or collecting, at or via a remote server (or processor) associated with an insurance provider, performance data associated with autonomous or semi-autonomous vehicle technology for an insured vehicle involved in a vehicle accident, the insured vehicle being insured by an insurance policy issued by the insurance provider; (2) analyzing, at or via the remote server, the performance data received; (3) determining, at or via the remote server, from analysis of the performance data: (a) a first percentage of fault of the vehicle accident for the autonomous or semi-autonomous vehicle technology of the insured vehicle in operation at a time of the vehicle accident; and/or (b) a second percentage of fault of the vehicle accident for an insured driver who was driving the insured vehicle during the vehicle accident; and/or (4) adjusting or updating, at or via the remote server, a premium, rate, discount, or reward of the insurance policy covering the insured vehicle based upon the first and second percentages of fault that are assigned to the insured vehicle and insured driver, respectively, from the analysis of the performance
  • the method may include further determining, at or via the remote server, from analysis of the performance data: (c) a third percentage of fault of the accident for other vehicles or drivers involved in and/or causing the vehicle accident.
  • the method may include handling, at or via the remote server, an insurance claim for the vehicle accident submitted by the insured using, or based upon, the performance data received associated with the autonomous or semi-autonomous vehicle functionality exhibited during the vehicle accident.
  • the method may include changing, at or via the remote server, policy coverages (and/or premiums, rates, discounts, etc.) for the insured or the insured vehicle using, or based upon, the performance data received associated with the autonomous or semi-autonomous vehicle functionality exhibited during the vehicle accident. Additionally or alternatively, the method may include changing, at or via the remote server, liability limits or coverages (and/or premiums, rates, discounts, etc.) for the insured or the insured vehicle using, or based upon, the performance data received associated with the autonomous or semi-autonomous vehicle functionality exhibited during the vehicle accident.
  • routines, subroutines, applications, or instructions may constitute either software (code embodied on a non-transitory, tangible machine-readable medium) or hardware.
  • routines, etc. are tangible units capable of performing certain operations and may be configured or arranged in a certain manner.
  • one or more computer systems e.g., a standalone, client or server computer system
  • one or more hardware modules of a computer system e.g., a processor or a group of processors
  • software e.g., an application or application portion
  • a hardware module may be implemented mechanically or electronically.
  • a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) to perform certain operations.
  • a hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
  • the term “hardware” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein.
  • hardware is temporarily configured (e.g., programmed)
  • the hardware need not be configured or instantiated at any one instance in time.
  • Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.
  • Hardware elements can provide information to, and receive information from, other hardware elements. Accordingly, the described hardware may be regarded as being communicatively coupled.
  • processors may be temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions.
  • the modules referred to herein may, in some example embodiments, comprise processor-implemented modules.
  • the methods or routines described herein may be at least partially processor-implemented.
  • the performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines.
  • the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.
  • Coupled and “connected” along with their derivatives.
  • some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact.
  • the term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.
  • the embodiments are not limited in this context.
  • the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion.
  • a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
  • “or” refers to an inclusive or and not to an exclusive or.
  • use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the description.

Abstract

Methods and systems for determining fault for an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features are provided. According to certain aspects, operating data from sensors within or near the vehicle may be used to determine fault for a vehicle accident, such as a collision. The operating data may include information regarding use of the features at the time of the accident and may further be used to determine an allocation of fault for the accident between a vehicle operator, the autonomous operation features, or a third party. The allocation of fault may be used to determine and/or adjust coverage levels for an insurance policy associated with the vehicle. The allocation of fault may further be used to adjust risk levels or profiles associated with the vehicle operator or with the autonomous operation features.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • This application is a continuation of, and claims the benefit of, U.S. patent application Ser. No. 14/713,254, filed May 15, 2015 and entitled “Accident Fault Determination for Autonomous Vehicles,” which claims the benefit of U.S. Provisional Application No. 62/000,878 (filed May 20, 2014); U.S. Provisional Application No. 62/018,169 (filed Jun. 27, 2014); U.S. Provisional Application No. 62/035,660 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,669 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,723 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,729 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,769 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,780 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,832 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,859 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,867 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,878 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,980 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/035,983 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/036,090 (filed Aug. 11, 2014); U.S. Provisional Application No. 62/047,307 (filed Sep. 8, 2014); and U.S. Provisional Application No. 62/056,893 (filed Sep. 29, 2014). The entirety of each of the foregoing provisional applications is incorporated by reference herein.
  • Additionally, the present application is related to U.S. patent application Ser. No. 14/951,774 (filed Nov. 25, 2015); U.S. patent application Ser. No. 14/713,184 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,188 (filed May 15, 2015); U.S. patent application Ser. No. 14/978,266 (filed Dec. 22, 2015); U.S. patent application Ser. No. 14/713,194 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,201 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,206 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,214 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,217 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,223 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,226 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,230 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,237 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,240 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,244 (filed May 15, 2015); U.S. patent application Ser. No. 14/713,249 (May 15, 2015); U.S. patent application Ser. No. 14/713,271 (filed May 15, 2015); U.S. patent application Ser. No. 14/951,803 (filed Nov. 25, 2015); U.S. patent application Ser. No. 14/713,261 (filed May 15, 2015); U.S. patent application Ser. No. 14/951,798 (filed Nov. 25, 2015); and U.S. patent application Ser. No. 14/713,266 (May 15, 2015). The entire contents of these related applications are hereby incorporated by reference in their entireties.
  • FIELD
  • The present disclosure generally relates to systems and methods for determining risk, pricing, and offering vehicle insurance policies, specifically vehicle insurance policies where vehicle operation is partially or fully automated.
  • BACKGROUND
  • Vehicle or automobile insurance exists to provide financial protection against physical damage and/or bodily injury resulting from traffic accidents and against liability that could arise therefrom. Typically, a customer purchases a vehicle insurance policy for a policy rate having a specified term. In exchange for payments from the insured customer, the insurer pays for damages to the insured which are caused by covered perils, acts, or events as specified by the language of the insurance policy. The payments from the insured are generally referred to as “premiums,” and typically are paid on behalf of the insured over time at periodic intervals. An insurance policy may remain “in-force” while premium payments are made during the term or length of coverage of the policy as indicated in the policy. An insurance policy may “lapse” (or have a status or state of “lapsed”), for example, when premium payments are not being paid or if the insured or the insurer cancels the policy.
  • Premiums may be typically determined based upon a selected level of insurance coverage, location of vehicle operation, vehicle model, and characteristics or demographics of the vehicle operator. The characteristics of a vehicle operator that affect premiums may include age, years operating vehicles of the same class, prior incidents involving vehicle operation, and losses reported by the vehicle operator to the insurer or a previous insurer. Past and current premium determination methods do not, however, account for use of autonomous vehicle operating features. The present embodiments may, inter alia, alleviate this and/or other drawbacks associated with conventional techniques.
  • BRIEF SUMMARY
  • The present embodiments may be related to autonomous or semi-autonomous vehicle functionality, including driverless operation, accident avoidance, or collision warning systems. These autonomous vehicle operation features may either assist the vehicle operator to more safely or efficiently operate a vehicle or may take full control of vehicle operation under some or all circumstances. The present embodiments may also facilitate risk assessment and premium determination for vehicle insurance policies covering vehicles with autonomous operation features.
  • In accordance with the described embodiments, the disclosure herein generally addresses systems and methods for determining risk levels associated with one or more autonomous (and/or semi-autonomous) operation features for controlling a vehicle or assisting a vehicle operator in controlling the vehicle. A server or other computer system may present test input signals to the one or more autonomous operation features to test the response of the features in a virtual environment. This virtual testing may include presentation of fixed inputs or may include a simulation of a dynamic virtual environment in which a virtual vehicle is controlled by the one or more autonomous operation features. The one or more autonomous operation features generate output signals that may then be used to determine the effectiveness of the control decisions by predicting the responses of vehicles to the output signals. Risk levels associated with the effectiveness of the autonomous operation features may be used to determine a premium for an insurance policy associated with the vehicle, which may be determined by reference to a risk category.
  • In one aspect, a computer-implemented method for testing the effectiveness of one or more autonomous operation features for controlling a virtual vehicle in a virtual test environment may be provided. The computer-implemented method may include receiving a set of computer-readable instructions for implementing the one or more autonomous operation features, executing the one or more software routines, receiving one or more test input signals that simulate the one or more signals from at least one sensor, generating one or more test output signals for the virtual vehicle in response to the received one or more test input signals, predicting one or more responses of the virtual vehicle in the virtual test environment to the one or more test output signals, and/or determining a measure of the effectiveness of the one or more autonomous operation features based upon the one or more predicted responses of the virtual vehicle to the one or more test output signals. The set of computer-readable instructions may include one or more software routines configured to receive one or more input signals from at least one sensor and generate one or more output signals for controlling a vehicle. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In another aspect, a computer system for testing the effectiveness of one or more autonomous operation features for controlling a virtual vehicle in a virtual test environment may be provided. The computer system may include one or more processors and a non-transitory program memory coupled to the one or more processors and storing executable instructions. The executable instruction may, when executed by the one or more processors, cause the computer system to receive a set of computer-readable instructions for implementing the one or more autonomous operation features, execute the one or more software routines, receive one or more test input signals that simulate the one or more signals from at least one sensor, generate one or more test output signals for the virtual vehicle in response to the received one or more test input signals, predict one or more responses of the virtual vehicle in the virtual test environment to the one or more test output signals, and/or determine a measure of the effectiveness of the one or more autonomous operation features based upon the one or more predicted responses of the virtual vehicle to the one or more test output signals. The set of computer-readable instructions may include one or more software routines configured to receive one or more input signals from at least one sensor and generate one or more output signals for controlling a vehicle. The system may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In yet another aspect, a tangible, non-transitory computer-readable medium storing instructions for testing the effectiveness of one or more autonomous operation features for controlling a virtual vehicle in a virtual test environment may be provided. The instructions may, when executed by at least one processor of a computer system, cause the computer system to receive a set of computer-readable instructions for implementing the one or more autonomous operation features, execute the one or more software routines, receive one or more test input signals that simulate the one or more signals from at least one sensor, generate one or more test output signals for the virtual vehicle in response to the received one or more test input signals, predict one or more responses of the virtual vehicle in the virtual test environment to the one or more test output signals, and/or determine a measure of the effectiveness of the one or more autonomous operation features based upon the one or more predicted responses of the virtual vehicle to the one or more test output signals. The set of computer-readable instructions may include one or more software routines configured to receive one or more input signals from at least one sensor and generate one or more output signals for controlling a vehicle. The instructions of the computer-readable medium may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In some systems or methods, the one or more test input signals may be received from a database containing a plurality of test signals. Alternatively, the test input signals may be received by generating a simulation of the virtual vehicle in the virtual test environment, determining simulated sensor data associated with the virtual vehicle in the virtual test environment, and determining the one or more test input signals based upon the simulated sensor data.
  • In further embodiments, the measure of the effectiveness of the one or more autonomous operation features may include one or more risk levels associated with autonomous operation of the virtual vehicle by the one or more autonomous operation features. Determining the measure of the effectiveness of the one or more autonomous operation features may also include determining the measure of the effectiveness of the one or more autonomous operation features in a plurality of virtual test environments. Each such virtual test environments may be based upon observed data regarding actual environments recorded by one or more sensors communicatively connected to a plurality of vehicles operating outside the virtual test environment.
  • Determining the one or more risk levels associated with the one or more autonomous operation features may include predicting the one or more risk levels based upon a comparison of (i) the one or more test output signals generated by the one or more software routines, (ii) one or more other test output signals generated by one or more other software routines of one or more other autonomous operation features in response to one or more other test input signals, and/or (iii) observed operating data regarding the one or more other autonomous operation features disposed within a plurality of other vehicles operating outside the virtual test environment. Additionally, the observed operating data may include data regarding actual losses associated with insurance policies covering the plurality of other vehicles having the one or more other autonomous operation features.
  • In accordance with the described embodiments, the disclosure herein also generally addresses systems and methods for monitoring the use of a vehicle having one or more autonomous (and/or semi-autonomous) operation features and determining risk associated with the one or more autonomous (and/or semi-autonomous) operation features based upon control decisions generated by the one or more autonomous (and/or semi-autonomous) operation features. An on-board computer or mobile device may monitor and record vehicle operating data, including information regarding the decisions made by the autonomous operation features, regardless of whether the decisions are actually used to control the vehicle. A server may receive the operating data and may process this data to determine risk levels associated with operation of the vehicle under the current conditions using a variety of available autonomous operation features, configurations, or settings.
  • In one aspect, a computer-implemented method for monitoring a vehicle having one or more autonomous operation features for controlling the vehicle may be provided. The computer-implemented method may include receiving operating data regarding operation of the vehicle, recording a log of the received operating data, receiving actual loss data regarding losses associated with insurance policies covering a plurality of other vehicles having the one or more autonomous operation features, and/or determining at least one risk level associated with the vehicle based at least in part upon the recorded log of the operating data and the received actual loss data. The operating data may include (i) information from one or more sensors disposed within the vehicle, (ii) information regarding the one or more autonomous operation features, and/or (iii) information regarding control decisions generated by the one or more autonomous operation features. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In another aspect, a computer system for monitoring a vehicle having one or more autonomous operation features for controlling the vehicle may be provided. The computer system may include one or more processors and a non-transitory program memory coupled to the one or more processors and storing executable instructions. The executable instruction may, when executed by the one or more processors, cause the computer system to receive operating data regarding operation of the vehicle, record a log of the received operating data, receive actual loss data regarding losses associated with insurance policies covering a plurality of other vehicles having the one or more autonomous operation features, and/or determine at least one risk level associated with the vehicle based at least in part upon the recorded log of the operating data and the received actual loss data. The operating data may include (i) information from one or more sensors disposed within the vehicle, (ii) information regarding the one or more autonomous operation features, and/or (iii) information regarding control decisions generated by the one or more autonomous operation features. The system may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In yet another aspect, a tangible, non-transitory computer-readable medium storing instructions for monitoring a vehicle having one or more autonomous operation features for controlling the vehicle may be provided. The instructions may, when executed by at least one processor of a computer system, cause the computer system to receive operating data regarding operation of the vehicle, record a log of the received operating data, receive actual loss data regarding losses associated with insurance policies covering a plurality of other vehicles having the one or more autonomous operation features, and/or determine at least one risk level associated with the vehicle based at least in part upon the recorded log of the operating data and the received actual loss data. The operating data may include (i) information from one or more sensors disposed within the vehicle, (ii) information regarding the one or more autonomous operation features, and/or (iii) information regarding control decisions generated by the one or more autonomous operation features. The instructions of the computer-readable medium may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In some embodiments, the information regarding the control decisions generated by the one or more autonomous operation features may include information regarding control decisions not implemented to control the vehicle, which may include the following: an alternative control decision not selected by the one or more autonomous operation features to control the vehicle and/or a control decision not implemented because the autonomous operation feature was disabled.
  • Each entry in the log of the operating data may include a timestamp associated with the recorded operating data, and each timestamp may include the following: date, time, location, vehicle environment, vehicle condition, autonomous operation feature settings, and/or autonomous operation feature configuration information. External data regarding the vehicle environment for each entry in the log of the operating data may be further included, including information regarding the following: road conditions, weather conditions, nearby traffic conditions, type of road, construction conditions, presence of pedestrians, presence of other obstacles, and/or availability of autonomous communications from external sources. The external data may be associated with log entries based upon the timestamp associated with each entry. In some embodiments, the at least one risk level associated with the vehicle may be further determined based at least in part upon the external data regarding the vehicle environment. Additionally, the operating data may be received by a mobile device within the vehicle. The mobile device may communicate the received operating data to a server via a network, and the server may record the log of the operating data.
  • Some systems or methods may further receive a request for a quote of a premium associated with a vehicle insurance policy and presenting an option to purchase the vehicle insurance policy to a customer associated with the vehicle. They may also determine a premium associated with the vehicle insurance policy based upon the at least one risk level.
  • In accordance with the described embodiments, the disclosure herein also generally addresses systems and methods for monitoring the use of a vehicle having one or more autonomous (and/or semi-autonomous) operation features and determining fault following the occurrence of an accident involving the vehicle. An on-board computer or mobile device may monitor and record vehicle operating data, including sensor data and data from the one or more autonomous operation features. A server may receive the operating data and may process this data to determine the cause of and fault for the accident. These fault determination may then be used, at least in part (i.e., wholly or partially), to determine coverage levels associated with an insurance policy associated with the vehicle and/or an adjustment to risk levels associated with the autonomous operation features.
  • In one aspect, a computer-implemented method for determining fault relating to a collision or other loss may be provided. The computer-implemented method may include receiving an indication of an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features for controlling the vehicle, receiving operating data regarding operation of the vehicle during a time period including the time of the accident, receiving information regarding use levels of the one or more autonomous operation features during the time period including the time of the accident, determining an allocation of fault for the accident based upon, at least in part (i.e., wholly or partially), the received operating data and the use levels of the one or more autonomous operation features, and/or determining one or more coverage levels associated with a vehicle insurance policy based upon, at least in part (i.e., wholly or partially), the determined allocation of fault. The indication of the accident may be generated based upon the received operating data. The operating data may include information from one or more sensors disposed within the vehicle and/or information regarding the operation of the one or more autonomous operation features. Additionally, the autonomous operation features may include one or more autonomous communication features, in which case the operating data may include communication data from external sources. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In another aspect, a computer system for determining fault relating to a collision or other loss may be provided. The computer system may include one or more processors, one or more communication modules adapted to communicate data, and a non-transitory program memory coupled to the one or more processors and storing executable instructions. The executable instruction may, when executed by the one or more processors, cause the computer system to receive an indication of an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features for controlling the vehicle, receive operating data regarding operation of the vehicle during a time period including the time of the accident, receive information regarding use levels of the one or more autonomous operation features during the time period including the time of the accident, determine an allocation of fault for the accident based upon the received operating data and the use levels of the one or more autonomous operation features, and/or determine one or more coverage levels associated with a vehicle insurance policy based upon the determined allocation of fault. The indication of the accident may be generated based upon the received operating data. The operating data may include information from one or more sensors disposed within the vehicle and/or information regarding the operation of the one or more autonomous operation features. Additionally, the autonomous operation features may include one or more autonomous communication features, in which case the operating data may include communication data from external sources. The system may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In yet another aspect, a tangible, non-transitory computer-readable medium storing instructions for determining fault relating to a collision or other loss may be provided. The instructions may, when executed by at least one processor of a computer system, cause the computer system to receive an indication of an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features for controlling the vehicle, receive operating data regarding operation of the vehicle during a time period including the time of the accident, receive information regarding use levels of the one or more autonomous operation features during the time period including the time of the accident, determine an allocation of fault for the accident based upon the received operating data and the use levels of the one or more autonomous operation features, and/or determine one or more coverage levels associated with a vehicle insurance policy based upon the determined allocation of fault. The indication of the accident may be generated based upon the received operating data. The operating data may include information from one or more sensors disposed within the vehicle and/or information regarding the operation of the one or more autonomous operation features. Additionally, the autonomous operation features may include one or more autonomous communication features, in which case the operating data may include communication data from external sources. The instructions of the computer-readable medium may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • Determining the allocation of fault may include allocating fault for the accident between one or more of the following: the vehicle operator, the one or more autonomous operation (and/or semi-autonomous) features as a group, each of the one or more autonomous (and/or semi-autonomous) operation features separately, and/or a third party. Allocating fault may further include determining one or more of the following: a point of impact on the vehicle, a point of impact on one or more additional vehicles, a velocity of the vehicle, a velocity of one or more additional vehicles, a movement of the vehicle, a movement of one or more additional vehicles, a location of one or more obstructions, a movement of one or more obstructions, a location of one or more pedestrians, a movement of one or more pedestrians, a measure of road surface integrity, a measure of road surface friction, a location of one or more traffic signs, a location of one or more traffic signals, an indication of a state of one or more traffic signals, a control signal generated by one or more autonomous operation features of the vehicle, and/or a control signal generated by one or more autonomous operation features of one or more additional vehicles.
  • In some embodiments, the one or more coverage levels may be determined based upon whether the determined allocation of fault indicates a vehicle operator is at least partially at fault for the accident. The coverage levels may be further determined based upon the proportion of fault allocated to the vehicle operator. The one or more coverage levels may include the following: a deductible, a type of coverage, a maximum coverage limit, an estimate of a cost to repair the vehicle, an estimate of a cost to replace the vehicle, an estimate of a cost to repair other property, an estimate of a cost to replace other property, and/or an estimate of a payment of medical expenses. In some embodiments, determining the one or more coverage levels may further include determining to cancel the vehicle insurance policy.
  • Some embodiments may include determining an adjustment to a cost associated with a vehicle insurance policy based upon the allocation of fault when the at least a portion of the fault is determined to be allocated to the vehicle operator. Additionally, some embodiments may include determining an adjustment to a risk level associated with one or more autonomous (and/or semi-autonomous) operation features based upon the allocation of fault when the at least a portion of the fault is determined to be allocated to the one or more autonomous (and/or semi-autonomous) operation features. The cost associated with the vehicle insurance policy may include a premium, a surcharge, a penalty, a rate, and/or a rate category. Other embodiments may include presenting the determined allocation of fault and/or the determined coverage levels to a reviewer for verification and/or receiving an indication of verification from the reviewer.
  • In some embodiments, the system or method may cause one or more options associated with the determined one or more coverage levels to be presented to a customer for review. A selection by the customer of at least one of the one or more options may be received, based upon which the vehicle insurance policy may be adjusted.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
  • The figures described below depict various aspects of the applications, methods, and systems disclosed herein. It should be understood that each figure depicts an embodiment of a particular aspect of the disclosed applications, systems and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Furthermore, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.
  • FIG. 1 illustrates a block diagram of an exemplary computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes in accordance with the described embodiments;
  • FIG. 2 illustrates a block diagram of an exemplary on-board computer or mobile device;
  • FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicle operation method in accordance with the presently described embodiments;
  • FIGS. 4A-B illustrate flow diagrams of an exemplary autonomous vehicle operation monitoring method in accordance with the presently described embodiments;
  • FIG. 5 illustrates a flow diagram of an exemplary autonomous operation feature evaluation method for determining the effectiveness of autonomous operation features in accordance with the presently described embodiments;
  • FIG. 6 illustrates a flow diagram of an exemplary autonomous operation feature testing method for presenting test conditions to an autonomous operation feature and observing and recording responses to the test conditions in accordance with the presently described embodiments;
  • FIG. 7 illustrates a flow diagram of an exemplary autonomous feature evaluation method for determining the effectiveness of an autonomous operation feature under a set of environmental conditions, configuration conditions, and settings in accordance with the presently described embodiments;
  • FIG. 8 illustrates a flow diagram depicting an exemplary embodiment of a fully autonomous vehicle insurance pricing method in accordance with the presently described embodiments;
  • FIG. 9 illustrates a flow diagram depicting an exemplary embodiment of a partially autonomous vehicle insurance pricing method in accordance with the presently described embodiments; and
  • FIG. 10 illustrates a flow diagram depicting an exemplary embodiment of an autonomous vehicle insurance pricing method for determining risk and premiums for vehicle insurance policies covering autonomous vehicles with autonomous communication features in accordance with the presently described embodiments; and
  • FIG. 11 illustrates a flow diagram of an exemplary autonomous operation feature monitoring and feedback method;
  • FIG. 12 illustrates a flow diagram of an exemplary autonomous operation feature monitoring and alert method;
  • FIG. 13 illustrates a flow diagram of an exemplary fault determination method for determining fault following an accident based upon sensor data and communication data; and
  • FIG. 14 illustrates a high-level flow diagram of an exemplary autonomous automobile insurance pricing system.
  • DETAILED DESCRIPTION
  • The systems and methods disclosed herein generally relate to evaluating, monitoring, pricing, and processing vehicle insurance policies for vehicles including autonomous (or semi-autonomous) vehicle operation features. The autonomous operation features may take full control of the vehicle under certain conditions, viz. fully autonomous operation, or the autonomous operation features may assist the vehicle operator in operating the vehicle, viz. partially autonomous operation. Fully autonomous operation features may include systems within the vehicle that pilot the vehicle to a destination with or without a vehicle operator present (e.g., an operating system for a driverless car). Partially autonomous operation features may assist the vehicle operator in limited ways (e.g., automatic braking or collision avoidance systems). The autonomous operation features may affect the risk related to operating a vehicle, both individually and/or in combination. To account for these effects on risk, some embodiments evaluate the quality of each autonomous operation feature and/or combination of features. This may be accomplished by testing the features and combinations in controlled environments, as well as analyzing the effectiveness of the features in the ordinary course of vehicle operation. New autonomous operation features may be evaluated based upon controlled testing and/or estimating ordinary-course performance based upon data regarding other similar features for which ordinary-course performance is known.
  • Some autonomous operation features may be adapted for use under particular conditions, such as city driving or highway driving. Additionally, the vehicle operator may be able to configure settings relating to the features or may enable or disable the features at will. Therefore, some embodiments monitor use of the autonomous operation features, which may include the settings or levels of feature use during vehicle operation. Information obtained by monitoring feature usage may be used to determine risk levels associated with vehicle operation, either generally or in relation to a vehicle operator. In such situations, total risk may be determined by a weighted combination of the risk levels associated with operation while autonomous operation features are enabled (with relevant settings) and the risk levels associated with operation while autonomous operation features are disabled. For fully autonomous vehicles, settings or configurations relating to vehicle operation may be monitored and used in determining vehicle operating risk.
  • Information regarding the risks associated with vehicle operation with and without the autonomous operation features may then be used to determine risk categories or premiums for a vehicle insurance policy covering a vehicle with autonomous operation features. Risk category or price may be determined based upon factors relating to the evaluated effectiveness of the autonomous vehicle features. The risk or price determination may also include traditional factors, such as location, vehicle type, and level of vehicle use. For fully autonomous vehicles, factors relating to vehicle operators may be excluded entirely. For partially autonomous vehicles, factors relating to vehicle operators may be reduced in proportion to the evaluated effectiveness and monitored usage levels of the autonomous operation features. For vehicles with autonomous communication features that obtain information from external sources (e.g., other vehicles or infrastructure), the risk level and/or price determination may also include an assessment of the availability of external sources of information. Location and/or timing of vehicle use may thus be monitored and/or weighted to determine the risk associated with operation of the vehicle.
  • Autonomous Automobile Insurance
  • The present embodiments may relate to assessing and pricing insurance based upon autonomous (or semi-autonomous) functionality of a vehicle, and not the human driver. A smart vehicle may maneuver itself without human intervention and/or include sensors, processors, computer instructions, and/or other components that may perform or direct certain actions conventionally performed by a human driver.
  • An analysis of how artificial intelligence facilitates avoiding accidents and/or mitigates the severity of accidents may be used to build a database and/or model of risk assessment. After which, automobile insurance risk and/or premiums (as well as insurance discounts, rewards, and/or points) may be adjusted based upon autonomous or semi-autonomous vehicle functionality, such as by groups of autonomous or semi-autonomous functionality or individual features. In one aspect, an evaluation may be performed of how artificial intelligence, and the usage thereof, impacts automobile accidents and/or automobile insurance claims.
  • The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality.
  • The adjustments to automobile insurance rates or premiums based upon the autonomous or semi-autonomous vehicle-related functionality or technology may take into account the impact of such functionality or technology on the likelihood of a vehicle accident or collision occurring. For instance, a processor may analyze historical accident information and/or test data involving vehicles having autonomous or semi-autonomous functionality. Factors that may be analyzed and/or accounted for that are related to insurance risk, accident information, or test data may include (1) point of impact; (2) type of road; (3) time of day; (4) weather conditions; (5) road construction; (6) type/length of trip; (7) vehicle style; (8) level of pedestrian traffic; (9) level of vehicle congestion; (10) atypical situations (such as manual traffic signaling); (11) availability of internet connection for the vehicle; and/or other factors. These types of factors may also be weighted according to historical accident information, predicted accidents, vehicle trends, test data, and/or other considerations.
  • In one aspect, the benefit of one or more autonomous or semi-autonomous functionalities or capabilities may be determined, weighted, and/or otherwise characterized. For instance, the benefit of certain autonomous or semi-autonomous functionality may be substantially greater in city or congested traffic, as compared to open road or country driving traffic. Additionally or alternatively, certain autonomous or semi-autonomous functionality may only work effectively below a certain speed, i.e., during city driving or driving in congestion. Other autonomous or semi-autonomous functionality may operate more effectively on the highway and away from city traffic, such as cruise control. Further individual autonomous or semi-autonomous functionality may be impacted by weather, such as rain or snow, and/or time of day (day light versus night). As an example, fully automatic or semi-automatic lane detection warnings may be impacted by rain, snow, ice, and/or the amount of sunlight (all of which may impact the imaging or visibility of lane markings painted onto a road surface, and/or road markers or street signs).
  • Automobile insurance premiums, rates, discounts, rewards, refunds, points, etc. may be adjusted based upon the percentage of time or vehicle usage that the vehicle is the driver, i.e., the amount of time a specific driver uses each type of autonomous (or even semi-autonomous) vehicle functionality. In other words, insurance premiums, discounts, rewards, etc. may be adjusted based upon the percentage of vehicle usage during which the autonomous or semi-autonomous functionality is in use. For example, automobile insurance risk, premiums, discounts, etc. for an automobile having one or more autonomous or semi-autonomous functionalities may be adjusted and/or set based upon the percentage of vehicle usage that the one or more individual autonomous or semi-autonomous vehicle functionalities are in use, anticipated to be used or employed by the driver, and/or otherwise operating.
  • Such usage information for a particular vehicle may be gathered over time and/or via remote wireless communication with the vehicle. One embodiment may involve a processor on the vehicle, such as within a vehicle control system or dashboard, monitoring in real-time whether vehicle autonomous or semi-autonomous functionality is currently operating. Other types of monitoring may be remotely performed, such as via wireless communication between the vehicle and a remote server, or wireless communication between a vehicle-mounted dedicated device (that is configured to gather autonomous or semi-autonomous functionality usage information) and a remote server.
  • In one embodiment, if the vehicle is currently employing autonomous or semi-autonomous functionality, the vehicle may send a Vehicle-to-Vehicle (V2V) wireless communication to a nearby vehicle also employing the same or other type(s) of autonomous or semi-autonomous functionality.
  • As an example, the V2V wireless communication from the first vehicle to the second vehicle (following the first vehicle) may indicate that the first vehicle is autonomously braking, and the degree to which the vehicle is automatically braking and/or slowing down. In response, the second vehicle may also automatically or autonomously brake as well, and the degree of automatically braking or slowing down of the second vehicle may be determined to match, or even exceed, that of the first vehicle. As a result, the second vehicle, traveling directly or indirectly, behind the first vehicle, may autonomously safely break in response to the first vehicle autonomously breaking.
  • As another example, the V2V wireless communication from the first vehicle to the second vehicle may indicate that the first vehicle is beginning or about to change lanes or turn. In response, the second vehicle may autonomously take appropriate action, such as automatically slow down, change lanes, turn, maneuver, etc. to avoid the first vehicle.
  • As noted above, the present embodiments may include remotely monitoring, in real-time and/or via wireless communication, vehicle autonomous or semi-autonomous functionality. From such remote monitoring, the present embodiments may remotely determine that a vehicle accident has occurred. As a result, emergency responders may be informed of the location of the vehicle accident, such as via wireless communication, and/or quickly dispatched to the accident scene.
  • The present embodiments may also include remotely monitoring, in real-time or via wireless communication, that vehicle autonomous or semi-autonomous functionality is, or is not, in use, and/or collect information regarding the amount of usage of the autonomous or semi-autonomous functionality. From such remote monitoring, a remote server may remotely send a wireless communication to the vehicle to prompt the human driver to engage one or more specific vehicle autonomous or semi-autonomous functionalities.
  • Another embodiment may enable a vehicle to wirelessly communicate with a traffic light, railroad crossing, toll both, marker, sign, or other equipment along the side of a road or highway. As an example, a traffic light may wirelessly indicate to the vehicle that the traffic light is about to switch from green to yellow, or from yellow to red. In response to such an indication remotely received from the traffic light, the autonomous or semi-autonomous vehicle may automatically start to brake, and/or present or issue a warning/alert to the human driver. After which, the vehicle may wirelessly communicate with the vehicles traveling behind it that the traffic light is about to change and/or that the vehicle has started to brake or slow down such that the following vehicles may also automatically brake or slow down accordingly.
  • Insurance premiums, rates, ratings, discounts, rewards, special offers, points, programs, refunds, claims, claim amounts, etc. may be adjusted for, or may otherwise take into account, the foregoing functionality and/or the other functionality described herein. For instance, insurance policies may be updated based upon autonomous or semi-autonomous vehicle functionality; V2V wireless communication-based autonomous or semi-autonomous vehicle functionality; and/or vehicle-to-infrastructure or infrastructure-to-vehicle wireless communication-based autonomous or semi-autonomous vehicle functionality.
  • Exemplary Embodiments
  • Insurance providers may currently develop a set of rating factors based upon the make, model, and model year of a vehicle. Models with better loss experience receive lower factors, and thus lower rates. One reason that this current rating system cannot be used to assess risk for autonomous technology is that many autonomous features vary for the same model. For example, two vehicles of the same model may have different hardware features for automatic braking, different computer instructions for automatic steering, and/or different artificial intelligence system versions. The current make and model rating may also not account for the extent to which another “driver,” in this case the vehicle itself, is controlling the vehicle.
  • The present embodiments may assess and price insurance risks at least in part based upon autonomous or semi-autonomous vehicle technology that replaces actions of the driver. In a way, the vehicle-related computer instructions and artificial intelligence may be viewed as a “driver.”
  • In one computer-implemented method of adjusting or generating an insurance policy, (1) data may be captured by a processor (such as via wireless communication) to determine the autonomous or semi-autonomous technology or functionality associated with a specific vehicle that is, or is to be, covered by insurance; (2) the received data may be compared by the processor to a stored baseline of vehicle data (such as actual accident information, and/or autonomous or semi-autonomous vehicle testing data); (3) risk may be identified or assessed by the processor based upon the specific vehicle's ability to make driving decisions and/or avoid or mitigate crashes; (4) an insurance policy may be adjusted (or generated or created), or an insurance premium may be determined by the processor based upon the risk identified that is associated with the specific vehicle's autonomous or semi-autonomous ability or abilities; and/or (5) the insurance policy and/or premium may be presented on a display or otherwise provided to the policyholder or potential customer for their review and/or approval. The method may include additional, fewer, or alternate actions, including those discussed below and elsewhere herein.
  • The method may include evaluating the effectiveness of artificial intelligence and/or vehicle technology in a test environment, and/or using real driving experience. The identification or assessment of risk performed by the method (and/or the processor) may be dependent upon the extent of control and decision making that is assumed by the vehicle, rather than the driver.
  • Additionally or alternatively, the identification or assessment of insurance and/or accident-based risk may be dependent upon the ability of the vehicle to use external information (such as vehicle-to-vehicle and vehicle-to-infrastructure communication) to make driving decisions. The risk assessment may further be dependent upon the availability of such external information. For instance, a vehicle (or vehicle owner) may be associated with a geographical location, such as a large city or urban area, where such external information is readily available via wireless communication. On the other hand, a small town or rural area may or may not have such external information available.
  • The information regarding the availability of autonomous or semi-autonomous vehicle technology, such as a particular factory-installed hardware and/or software package, version, revision, or update, may be wirelessly transmitted to a remote server for analysis. The remote server may be associated with an insurance provider, vehicle manufacturer, autonomous technology provider, and/or other entity.
  • The driving experience and/or usage of the autonomous or semi-autonomous vehicle technology may be monitored in real time, small timeframes, and/or periodically to provide feedback to the driver, insurance provider, and/or adjust insurance policies or premiums. In one embodiment, information may be wirelessly transmitted to the insurance provider, such as from a transceiver associated with a smart car to an insurance provider remote server.
  • Insurance policies, including insurance premiums, discounts, and rewards, may be updated, adjusted, and/or determined based upon hardware or software functionality, and/or hardware or software upgrades. Insurance policies, including insurance premiums, discounts, etc. may also be updated, adjusted, and/or determined based upon the amount of usage and/or the type(s) of the autonomous or semi-autonomous technology employed by the vehicle.
  • In one embodiment, performance of autonomous driving software and/or sophistication of artificial intelligence may be analyzed for each vehicle. An automobile insurance premium may be determined by evaluating how effectively the vehicle may be able to avoid and/or mitigate crashes and/or the extent to which the driver's control of the vehicle is enhanced or replaced by the vehicle's software and artificial intelligence.
  • When pricing a vehicle with autonomous driving technology, artificial intelligence capabilities, rather than human decision making, may be evaluated to determine the relative risk of the insurance policy. This evaluation may be conducted using multiple techniques. Vehicle technology may be assessed in a test environment, in which the ability of the artificial intelligence to detect and avoid potential crashes may be demonstrated experimentally. For example, this may include a vehicle's ability to detect a slow-moving vehicle ahead and/or automatically apply the brakes to prevent a collision.
  • Additionally, actual loss experience of the software in question may be analyzed. Vehicles with superior artificial intelligence and crash avoidance capabilities may experience lower insurance losses in real driving situations.
  • Results from both the test environment and/or actual insurance losses may be compared to the results of other autonomous software packages and/or vehicles lacking autonomous driving technology to determine a relative risk factor (or level of risk) for the technology in question. This risk factor (or level of risk) may be applicable to other vehicles that utilize the same or similar autonomous operation software package(s).
  • Emerging technology, such as new iterations of artificial intelligence systems, may be priced by combining its individual test environment assessment with actual losses corresponding to vehicles with similar autonomous operation software packages. The entire vehicle software and artificial intelligence evaluation process may be conducted with respect to various technologies and/or elements that affect driving experience. For example, a fully autonomous vehicle may be evaluated based on its vehicle-to-vehicle communications. A risk factor could then be determined and applied when pricing the vehicle. The driver's past loss experience and/or other driver risk characteristics may not be considered for fully autonomous vehicles, in which all driving decisions are made by the vehicle's artificial intelligence.
  • In one embodiment, a separate portion of the automobile insurance premium may be based explicitly on the artificial intelligence software's driving performance and characteristics. The artificial intelligence pricing model may be combined with traditional methods for semi-autonomous vehicles. Insurance pricing for fully autonomous, or driverless, vehicles may be based upon the artificial intelligence model score by excluding traditional rating factors that measure risk presented by the drivers. Evaluation of vehicle software and/or artificial intelligence may be conducted on an aggregate basis or for specific combinations of technology and/or driving factors or elements (as discussed elsewhere herein). The vehicle software test results may be combined with actual loss experience to determine relative risk.
  • Exemplary Autonomous Vehicle Operation System
  • FIG. 1 illustrates a block diagram of an exemplary autonomous vehicle insurance system 100 on which the exemplary methods described herein may be implemented. The high-level architecture includes both hardware and software applications, as well as various data communications channels for communicating data between the various hardware and software components. The autonomous vehicle insurance system 100 may be roughly divided into front-end components 102 and back-end components 104. The front-end components 102 may obtain information regarding a vehicle 108 (e.g., a car, truck, motorcycle, etc.) and the surrounding environment. An on-board computer 114 may utilize this information to operate the vehicle 108 according to an autonomous operation feature or to assist the vehicle operator in operating the vehicle 108. To monitor the vehicle 108, the front-end components 102 may include one or more sensors 120 installed within the vehicle 108 that may communicate with the on-board computer 114. The front-end components 102 may further process the sensor data using the on-board computer 114 or a mobile device 110 (e.g., a smart phone, a tablet computer, a special purpose computing device, etc.) to determine when the vehicle is in operation and information regarding the vehicle. In some embodiments of the system 100, the front-end components 102 may communicate with the back-end components 104 via a network 130. Either the on-board computer 114 or the mobile device 110 may communicate with the back-end components 104 via the network 130 to allow the back-end components 104 to record information regarding vehicle usage. The back-end components 104 may use one or more servers 140 to receive data from the front-end components 102, determine use and effectiveness of autonomous operation features, determine risk levels or premium price, and/or facilitate purchase or renewal of an autonomous vehicle insurance policy.
  • The front-end components 102 may be disposed within or communicatively connected to one or more on-board computers 114, which may be permanently or removably installed in the vehicle 108. The on-board computer 114 may interface with the one or more sensors 120 within the vehicle 108 (e.g., an ignition sensor, an odometer, a system clock, a speedometer, a tachometer, an accelerometer, a gyroscope, a compass, a geolocation unit, a camera, a distance sensor, etc.), which sensors may also be incorporated within or connected to the on-board computer 114. The front end components 102 may further include a communication component 122 to transmit information to and receive information from external sources, including other vehicles, infrastructure, or the back-end components 104. In some embodiments, the mobile device 110 may supplement the functions performed by the on-board computer 114 described herein by, for example, sending or receiving information to and from the mobile server 140 via the network 130. In other embodiments, the on-board computer 114 may perform all of the functions of the mobile device 110 described herein, in which case no mobile device 110 may be present in the system 100. Either or both of the mobile device 110 or on-board computer 114 may communicate with the network 130 over links 112 and 118, respectively. Additionally, the mobile device 110 and on-board computer 114 may communicate with one another directly over link 116.
  • The mobile device 110 may be either a general-use personal computer, cellular phone, smart phone, tablet computer, or a dedicated vehicle use monitoring device. Although only one mobile device 110 is illustrated, it should be understood that a plurality of mobile devices 110 may be used in some embodiments. The on-board computer 114 may be a general-use on-board computer capable of performing many functions relating to vehicle operation or a dedicated computer for autonomous vehicle operation. Further, the on-board computer 114 may be installed by the manufacturer of the vehicle 108 or as an aftermarket modification or addition to the vehicle 108. In some embodiments or under certain conditions, the mobile device 110 or on-board computer 114 may function as thin-client devices that outsource some or most of the processing to the server 140.
  • The sensors 120 may be removably or fixedly installed within the vehicle 108 and may be disposed in various arrangements to provide information to the autonomous operation features. Among the sensors 120 may be included one or more of a GPS unit, a radar unit, a LIDAR unit, an ultrasonic sensor, an infrared sensor, a camera, an accelerometer, a tachometer, or a speedometer. Some of the sensors 120 (e.g., radar, LIDAR, or camera units) may actively or passively scan the vehicle environment for obstacles (e.g., other vehicles, buildings, pedestrians, etc.), lane markings, or signs or signals. Other sensors 120 (e.g., GPS, accelerometer, or tachometer units) may provide data for determining the location or movement of the vehicle 108. Other sensors 120 may be directed to the interior or passenger compartment of the vehicle 108, such as cameras, microphones, pressure sensors, thermometers, or similar sensors to monitor the vehicle operator and/or passengers within the vehicle 108. Information generated or received by the sensors 120 may be communicated to the on-board computer 114 or the mobile device 110 for use in autonomous vehicle operation.
  • In some embodiments, the communication component 122 may receive information from external sources, such as other vehicles or infrastructure. The communication component 122 may also send information regarding the vehicle 108 to external sources. To send and receive information, the communication component 122 may include a transmitter and a receiver designed to operate according to predetermined specifications, such as the dedicated short-range communication (DSRC) channel, wireless telephony, Wi-Fi, or other existing or later-developed communications protocols. The received information may supplement the data received from the sensors 120 to implement the autonomous operation features. For example, the communication component 122 may receive information that an autonomous vehicle ahead of the vehicle 108 is reducing speed, allowing the adjustments in the autonomous operation of the vehicle 108.
  • In addition to receiving information from the sensors 120, the on-board computer 114 may directly or indirectly control the operation of the vehicle 108 according to various autonomous operation features. The autonomous operation features may include software applications or modules implemented by the on-board computer 114 to control the steering, braking, or throttle of the vehicle 108. To facilitate such control, the on-board computer 114 may be communicatively connected to the controls or components of the vehicle 108 by various electrical or electromechanical control components (not shown). In embodiments involving fully autonomous vehicles, the vehicle 108 may be operable only through such control components (not shown). In other embodiments, the control components may be disposed within or supplement other vehicle operator control components (not shown), such as steering wheels, accelerator or brake pedals, or ignition switches.
  • In some embodiments, the front-end components 102 communicate with the back-end components 104 via the network 130. The network 130 may be a proprietary network, a secure public internet, a virtual private network or some other type of network, such as dedicated access lines, plain ordinary telephone lines, satellite links, cellular data networks, combinations of these. Where the network 130 comprises the Internet, data communications may take place over the network 130 via an Internet communication protocol. The back-end components 104 include one or more servers 140. Each server 140 may include one or more computer processors adapted and configured to execute various software applications and components of the autonomous vehicle insurance system 100, in addition to other software applications. The server 140 may further include a database 146, which may be adapted to store data related to the operation of the vehicle 108 and its autonomous operation features. Such data might include, for example, dates and times of vehicle use, duration of vehicle use, use and settings of autonomous operation features, speed of the vehicle 108, RPM or other tachometer readings of the vehicle 108, lateral and longitudinal acceleration of the vehicle 108, incidents or near collisions of the vehicle 108, communication between the autonomous operation features and external sources, environmental conditions of vehicle operation (e.g., weather, traffic, road condition, etc.), errors or failures of autonomous operation features, or other data relating to use of the vehicle 108 and the autonomous operation features, which may be uploaded to the server 140 via the network 130. The server 140 may access data stored in the database 146 when executing various functions and tasks associated with the evaluating feature effectiveness or assessing risk relating to an autonomous vehicle.
  • Although the autonomous vehicle insurance system 100 is shown to include one vehicle 108, one mobile device 110, one on-board computer 114, and one server 140, it should be understood that different numbers of vehicles 108, mobile devices 110, on-board computers 114, and/or servers 140 may be utilized. For example, the system 100 may include a plurality of servers 140 and hundreds of mobile devices 110 or on-board computers 114, all of which may be interconnected via the network 130. Furthermore, the database storage or processing performed by the one or more servers 140 may be distributed among a plurality of servers 140 in an arrangement known as “cloud computing.” This configuration may provide various advantages, such as enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information. This may in turn support a thin-client embodiment of the mobile device 110 or on-board computer 114 discussed herein.
  • The server 140 may have a controller 155 that is operatively connected to the database 146 via a link 156. It should be noted that, while not shown, additional databases may be linked to the controller 155 in a known manner. For example, separate databases may be used for autonomous operation feature information, vehicle insurance policy information, and vehicle use information. The controller 155 may include a program memory 160, a processor 162 (which may be called a microcontroller or a microprocessor), a random-access memory (RAM) 164, and an input/output (I/O) circuit 166, all of which may be interconnected via an address/data bus 165. It should be appreciated that although only one microprocessor 162 is shown, the controller 155 may include multiple microprocessors 162. Similarly, the memory of the controller 155 may include multiple RAMs 164 and multiple program memories 160. Although the I/O circuit 166 is shown as a single block, it should be appreciated that the I/O circuit 166 may include a number of different types of I/O circuits. The RAM 164 and program memories 160 may be implemented as semiconductor memories, magnetically readable memories, or optically readable memories, for example. The controller 155 may also be operatively connected to the network 130 via a link 135.
  • The server 140 may further include a number of software applications stored in a program memory 160. The various software applications on the server 140 may include an autonomous operation information monitoring application 141 for receiving information regarding the vehicle 108 and its autonomous operation features, a feature evaluation application 142 for determining the effectiveness of autonomous operation features under various conditions, a compatibility evaluation application 143 for determining the effectiveness of combinations of autonomous operation features, a risk assessment application 144 for determining a risk category associated with an insurance policy covering an autonomous vehicle, and an autonomous vehicle insurance policy purchase application 145 for offering and facilitating purchase or renewal of an insurance policy covering an autonomous vehicle. The various software applications may be executed on the same computer processor or on different computer processors.
  • FIG. 2 illustrates a block diagram of an exemplary mobile device 110 or an exemplary on-board computer 114 consistent with the system 100. The mobile device 110 or on-board computer 114 may include a display 202, a GPS unit 206, a communication unit 220, an accelerometer 224, one or more additional sensors (not shown), a user-input device (not shown), and/or, like the server 140, a controller 204. In some embodiments, the mobile device 110 and on-board computer 114 may be integrated into a single device, or either may perform the functions of both. The on-board computer 114 (or mobile device 110) interfaces with the sensors 120 to receive information regarding the vehicle 108 and its environment, which information is used by the autonomous operation features to operate the vehicle 108.
  • Similar to the controller 155, the controller 204 may include a program memory 208, one or more microcontrollers or microprocessors (MP) 210, a RAM 212, and an I/O circuit 216, all of which are interconnected via an address/data bus 214. The program memory 208 includes an operating system 226, a data storage 228, a plurality of software applications 230, and/or a plurality of software routines 240. The operating system 226, for example, may include one of a plurality of general purpose or mobile platforms, such as the Android™, iOS®, or Windows® systems, developed by Google Inc., Apple Inc., and Microsoft Corporation, respectively. Alternatively, the operating system 226 may be a custom operating system designed for autonomous vehicle operation using the on-board computer 114. The data storage 228 may include data such as user profiles and preferences, application data for the plurality of applications 230, routine data for the plurality of routines 240, and other data related to the autonomous operation features. In some embodiments, the controller 204 may also include, or otherwise be communicatively connected to, other data storage mechanisms (e.g., one or more hard disk drives, optical storage drives, solid state storage devices, etc.) that reside within the vehicle 108.
  • As discussed with reference to the controller 155, it should be appreciated that although FIG. 2 depicts only one microprocessor 210, the controller 204 may include multiple microprocessors 210. Similarly, the memory of the controller 204 may include multiple RAMs 212 and multiple program memories 208. Although FIG. 2 depicts the I/O circuit 216 as a single block, the I/O circuit 216 may include a number of different types of I/O circuits. The controller 204 may implement the RAMs 212 and the program memories 208 as semiconductor memories, magnetically readable memories, or optically readable memories, for example.
  • The one or more processors 210 may be adapted and configured to execute any of one or more of the plurality of software applications 230 or any one or more of the plurality of software routines 240 residing in the program memory 204, in addition to other software applications. One of the plurality of applications 230 may be an autonomous vehicle operation application 232 that may be implemented as a series of machine-readable instructions for performing the various tasks associated with implementing one or more of the autonomous operation features according to the autonomous vehicle operation method 300. Another of the plurality of applications 230 may be an autonomous communication application 234 that may be implemented as a series of machine-readable instructions for transmitting and receiving autonomous operation information to or from external sources via the communication module 220. Still another application of the plurality of applications 230 may include an autonomous operation monitoring application 236 that may be implemented as a series of machine-readable instructions for sending information regarding autonomous operation of the vehicle to the server 140 via the network 130.
  • The plurality of software applications 230 may call various of the plurality of software routines 240 to perform functions relating to autonomous vehicle operation, monitoring, or communication. One of the plurality of software routines 240 may be a configuration routine 242 to receive settings from the vehicle operator to configure the operating parameters of an autonomous operation feature. Another of the plurality of software routines 240 may be a sensor control routine 244 to transmit instructions to a sensor 120 and receive data from the sensor 120. Still another of the plurality of software routines 240 may be an autonomous control routine 246 that performs a type of autonomous control, such as collision avoidance, lane centering, or speed control. In some embodiments, the autonomous vehicle operation application 232 may cause a plurality of autonomous control routines 246 to determine control actions required for autonomous vehicle operation. Similarly, one of the plurality of software routines 240 may be a monitoring and reporting routine 248 that transmits information regarding autonomous vehicle operation to the server 140 via the network 130. Yet another of the plurality of software routines 240 may be an autonomous communication routine 250 for receiving and transmitting information between the vehicle 108 and external sources to improve the effectiveness of the autonomous operation features. Any of the plurality of software applications 230 may be designed to operate independently of the software applications 230 or in conjunction with the software applications 230.
  • When implementing the exemplary autonomous vehicle operation method 300, the controller 204 of the on-board computer 114 may implement the autonomous vehicle operation application 232 to communicate with the sensors 120 to receive information regarding the vehicle 108 and its environment and process that information for autonomous operation of the vehicle 108. In some embodiments including external source communication via the communication component 122 or the communication unit 220, the controller 204 may further implement the autonomous communication application 234 to receive information for external sources, such as other autonomous vehicles, smart infrastructure (e.g., electronically communicating roadways, traffic signals, or parking structures), or other sources of relevant information (e.g., weather, traffic, local amenities). Some external sources of information may be connected to the controller 204 via the network 130, such as the server 140 or internet-connected third-party databases (not shown). Although the autonomous vehicle operation application 232 and the autonomous communication application 234 are shown as two separate applications, it should be understood that the functions of the autonomous operation features may be combined or separated into any number of the software applications 230 or the software routines 240.
  • When implementing the autonomous operation feature monitoring and evaluation methods 400-700, the controller 204 may further implement the autonomous operation monitoring application 236 to communicate with the server 140 to provide information regarding autonomous vehicle operation. This may include information regarding settings or configurations of autonomous operation features, data from the sensors 120 regarding the vehicle environment, data from the sensors 120 regarding the response of the vehicle 108 to its environment, communications sent or received using the communication component 122 or the communication unit 220, operating status of the autonomous vehicle operation application 232 and the autonomous communication application 234, or commands sent from the on-board computer 114 to the control components (not shown) to operate the vehicle 108. The information may be received and stored by the server 140 implementing the autonomous operation information monitoring application 141, and the server 140 may then determine the effectiveness of autonomous operation under various conditions by implementing the feature evaluation application 142 and the compatibility evaluation application 143. The effectiveness of autonomous operation features and the extent of their use may be further used to determine risk associated with operation of the autonomous vehicle by the server 140 implementing the risk assessment application 144.
  • In addition to connections to the sensors 120, the mobile device 110 or the on-board computer 114 may include additional sensors, such as the GPS unit 206 or the accelerometer 224, which may provide information regarding the vehicle 108 for autonomous operation and other purposes. Furthermore, the communication unit 220 may communicate with other autonomous vehicles, infrastructure, or other external sources of information to transmit and receive information relating to autonomous vehicle operation. The communication unit 220 may communicate with the external sources via the network 130 or via any suitable wireless communication protocol network, such as wireless telephony (e.g., GSM, CDMA, LTE, etc.), Wi-Fi (802.11 standards), WiMAX, Bluetooth, infrared or radio frequency communication, etc. Furthermore, the communication unit 220 may provide input signals to the controller 204 via the I/O circuit 216. The communication unit 220 may also transmit sensor data, device status information, control signals, or other output from the controller 204 to one or more external sensors within the vehicle 108, mobile devices 110, on-board computers 114, or servers 140.
  • The mobile device 110 or the on-board computer 114 may include a user-input device (not shown) for receiving instructions or information from the vehicle operator, such as settings relating to an autonomous operation feature. The user-input device (not shown) may include a “soft” keyboard that is displayed on the display 202, an external hardware keyboard communicating via a wired or a wireless connection (e.g., a Bluetooth keyboard), an external mouse, a microphone, or any other suitable user-input device. The user-input device (not shown) may also include a microphone capable of receiving user voice input.
  • Exemplary Autonomous Vehicle Operation Method
  • FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicle operation method 300, which may be implemented by the autonomous vehicle insurance system 100. The method 300 may begin at block 302 when the controller 204 receives a start signal. The start signal may be a command from the vehicle operator through the user-input device to enable or engage one or more autonomous operation features of the vehicle 108. In some embodiments, the vehicle operator 108 may further specify settings or configuration details for the autonomous operation features. For fully autonomous vehicles, the settings may relate to one or more destinations, route preferences, fuel efficiency preferences, speed preferences, or other configurable settings relating to the operation of the vehicle 108. In some embodiments, fully autonomous vehicles may include additional features or settings permitting them to operate without passengers or vehicle operators within the vehicle. For example, a fully autonomous vehicle may receive an instruction to find a parking space within the general vicinity, which the vehicle may do without the vehicle operator. The vehicle may then be returned to a selected location by a request from the vehicle operator via a mobile device 110 or otherwise. This feature may further be adapted to return a fully autonomous vehicle if lost or stolen.
  • For other autonomous vehicles, the settings may include enabling or disabling particular autonomous operation features, specifying thresholds for autonomous operation, specifying warnings or other information to be presented to the vehicle operator, specifying autonomous communication types to send or receive, specifying conditions under which to enable or disable autonomous operation features, or specifying other constraints on feature operation. For example, a vehicle operator may set the maximum speed for an adaptive cruise control feature with automatic lane centering. In some embodiments, the settings may further include a specification of whether the vehicle 108 should be operating as a fully or partially autonomous vehicle. In embodiments where only one autonomous operation feature is enabled, the start signal may consist of a request to perform a particular task (e.g., autonomous parking) or to enable a particular feature (e.g., autonomous braking for collision avoidance). In other embodiments, the start signal may be generated automatically by the controller 204 based upon predetermined settings (e.g., when the vehicle 108 exceeds a certain speed or is operating in low-light conditions). In some embodiments, the controller 204 may generate a start signal when communication from an external source is received (e.g., when the vehicle 108 is on a smart highway or near another autonomous vehicle).
  • After receiving the start signal at block 302, the controller 204 receives sensor data from the sensors 120 during vehicle operation at block 304. In some embodiments, the controller 204 may also receive information from external sources through the communication component 122 or the communication unit 220. The sensor data may be stored in the RAM 212 for use by the autonomous vehicle operation application 232. In some embodiments, the sensor data may be recorded in the data storage 228 or transmitted to the server 140 via the network 130. The sensor data may alternately either be received by the controller 204 as raw data measurements from one of the sensors 120 or may be preprocessed by the sensor 120 prior to being received by the controller 204. For example, a tachometer reading may be received as raw data or may be preprocessed to indicate vehicle movement or position. As another example, a sensor 120 comprising a radar or LIDAR unit may include a processor to preprocess the measured signals and send data representing detected objects in 3-dimensional space to the controller 204.
  • The autonomous vehicle operation application 232 or other applications 230 or routines 240 may cause the controller 204 to process the received sensor data at block 306 in accordance with the autonomous operation features. The controller 204 may process the sensor data to determine whether an autonomous control action is required or to determine adjustments to the controls of the vehicle 108. For example, the controller 204 may receive sensor data indicating a decreasing distance to a nearby object in the vehicle's path and process the received sensor data to determine whether to begin braking (and, if so, how abruptly to slow the vehicle 108). As another example, the controller 204 may process the sensor data to determine whether the vehicle 108 is remaining with its intended path (e.g., within lanes on a roadway). If the vehicle 108 is beginning to drift or slide (e.g., as on ice or water), the controller 204 may determine appropriate adjustments to the controls of the vehicle to maintain the desired bearing. If the vehicle 108 is moving within the desired path, the controller 204 may nonetheless determine whether adjustments are required to continue following the desired route (e.g., following a winding road). Under some conditions, the controller 204 may determine to maintain the controls based upon the sensor data (e.g., when holding a steady speed on a straight road).
  • When the controller 204 determines an autonomous control action is required at block 308, the controller 204 may cause the control components of the vehicle 108 to adjust the operating controls of the vehicle to achieve desired operation at block 310. For example, the controller 204 may send a signal to open or close the throttle of the vehicle 108 to achieve a desired speed. Alternatively, the controller 204 may control the steering of the vehicle 108 to adjust the direction of movement. In some embodiments, the vehicle 108 may transmit a message or indication of a change in velocity or position using the communication component 122 or the communication module 220, which signal may be used by other autonomous vehicles to adjust their controls. As discussed further below, the controller 204 may also log or transmit the autonomous control actions to the server 140 via the network 130 for analysis.
  • The controller 204 may continue to receive and process sensor data at blocks 304 and 306 until an end signal is received by the controller 204 at block 312. The end signal may be automatically generated by the controller 204 upon the occurrence of certain criteria (e.g., the destination is reached or environmental conditions require manual operation of the vehicle 108 by the vehicle operator). Alternatively, the vehicle operator may pause, terminate, or disable the autonomous operation feature or features using the user-input device or by manually operating the vehicle's controls, such as by depressing a pedal or turning a steering instrument. When the autonomous operation features are disabled or terminated, the controller 204 may either continue vehicle operation without the autonomous features or may shut off the vehicle 108, depending upon the circumstances.
  • Where control of the vehicle 108 must be returned to the vehicle operator, the controller 204 may alert the vehicle operator in advance of returning to manual operation. The alert may include a visual, audio, or other indication to obtain the attention of the vehicle operator. In some embodiments, the controller 204 may further determine whether the vehicle operator is capable of resuming manual operation before terminating autonomous operation. If the vehicle operator is determined not be capable of resuming operation, the controller 204 may cause the vehicle to stop or take other appropriate action.
  • Exemplary Monitoring Method
  • FIG. 4A is a flow diagram depicting an exemplary autonomous vehicle operation monitoring method 400, which may be implemented by the autonomous vehicle insurance system 100. The method 400 monitors the operation of the vehicle 108 and transmits information regarding the vehicle 108 to the server 140, which information may then be used to determine autonomous operation feature effectiveness or usage rates to assess risk and price vehicle insurance policy premiums. The method 400 may be used both for testing autonomous operation features in a controlled environment of for determining feature use by an insured party. In alternative embodiments, the method 400 may be implemented whenever the vehicle 108 is in operation (manual or autonomous) or only when the autonomous operation features are enabled. The method 400 may likewise be implemented as either a real-time process, in which information regarding the vehicle 108 is communicated to the server 140 while monitoring is ongoing, or as a periodic process, in which the information is stored within the vehicle 108 and communicated to the server 140 at intervals (e.g., upon completion of a trip or when an incident occurs). In some embodiments, the method 400 may communicate with the server 140 in real-time when certain conditions exist (e.g., when a sufficient data connection through the network 130 exists or when no roaming charges would be incurred).
  • The method 400 may begin at block 402 when the controller 204 receives an indication of vehicle operation. The indication may be generated when the vehicle 108 is started or when an autonomous operation feature is enabled by the controller 204 or by input from the vehicle operator. In response to receiving the indication, the controller 204 may create a timestamp at block 404. The timestamp may include information regarding the date, time, location, vehicle environment, vehicle condition, and autonomous operation feature settings or configuration information. The date and time may be used to identify one vehicle trip or one period of autonomous operation feature use, in addition to indicating risk levels due to traffic or other factors. The additional location and environmental data may include information regarding the position of the vehicle 108 from the GPS unit 206 and its surrounding environment (e.g., road conditions, weather conditions, nearby traffic conditions, type of road, construction conditions, presence of pedestrians, presence of other obstacles, availability of autonomous communications from external sources, etc.). Vehicle condition information may include information regarding the type, make, and model of the vehicle 108, the age or mileage of the vehicle 108, the status of vehicle equipment (e.g., tire pressure, non-functioning lights, fluid levels, etc.), or other information relating to the vehicle 108. In some embodiments, the timestamp may be recorded on the client device 114, the mobile device 110, or the server 140.
  • The autonomous operation feature settings may correspond to information regarding the autonomous operation features, such as those described above with reference to the autonomous vehicle operation method 300. The autonomous operation feature configuration information may correspond to information regarding the number and type of the sensors 120, the disposition of the sensors 120 within the vehicle 108, the one or more autonomous operation features (e.g., the autonomous vehicle operation application 232 or the software routines 240), autonomous operation feature control software, versions of the software applications 230 or routines 240 implementing the autonomous operation features, or other related information regarding the autonomous operation features. For example, the configuration information may include the make and model of the vehicle 108 (indicating installed sensors 120 and the type of on-board computer 114), an indication of a malfunctioning or obscured sensor 120 in part of the vehicle 108, information regarding additional after-market sensors 120 installed within the vehicle 108, a software program type and version for a control program installed as an application 230 on the on-board computer 114, and software program types and versions for each of a plurality of autonomous operation features installed as applications 230 or routines 240 in the program memory 208 of the on-board computer 114.
  • During operation, the sensors 120 may generate sensor data regarding the vehicle 108 and its environment. In some embodiments, one or more of the sensors 120 may preprocess the measurements and communicate the resulting processed data to the on-board computer 114. The controller 204 may receive sensor data from the sensors 120 at block 406. The sensor data may include information regarding the vehicle's position, speed, acceleration, direction, and responsiveness to controls. The sensor data may further include information regarding the location and movement of obstacles or obstructions (e.g., other vehicles, buildings, barriers, pedestrians, animals, trees, or gates), weather conditions (e.g., precipitation, wind, visibility, or temperature), road conditions (e.g., lane markings, potholes, road material, traction, or slope), signs or signals (e.g., traffic signals, construction signs, building signs or numbers, or control gates), or other information relating to the vehicle's environment. In some embodiments, sensors 120 may indicate the number of passengers within the vehicle 108, including an indication of whether the vehicle is entirely empty.
  • In addition to receiving sensor data from the sensors 120, in some embodiments the controller 204 may receive autonomous communication data from the communication component 122 or the communication module 220 at block 408. The communication data may include information from other autonomous vehicles (e.g., sudden changes to vehicle speed or direction, intended vehicle paths, hard braking, vehicle failures, collisions, or maneuvering or stopping capabilities), infrastructure (road or lane boundaries, bridges, traffic signals, control gates, or emergency stopping areas), or other external sources (e.g., map databases, weather databases, or traffic and accident databases). The communication data may be combined with the sensor data received at block 406 to obtain a more robust understanding of the vehicle environment. For example, the server 140 or the controller 204 may combine sensor data indicating frequent changes in speed relative to tachometric data with map data relating to a road upon which the vehicle 108 is traveling to determine that the vehicle 108 is in an area of hilly terrain. As another example, weather data indicating recent snowfall in the vicinity of the vehicle 108 may be combined with sensor data indicating frequent slipping or low traction to determine that the vehicle 108 is traveling on a snow-covered or icy road.
  • At block 410, the controller 204 may process the sensor data, the communication data, and the settings or configuration information to determine whether an incident has occurred. As used herein, an “incident” is an occurrence during operation of an autonomous vehicle outside of normal safe operating conditions, such that one or more of the following occurs: (i) there is an interruption of vehicle operation, (ii) there is damage to the vehicle or other property, (iii) there is injury to a person, and/or (iv) the conditions require action to be taken by a vehicle operator, autonomous operation feature, pedestrian, or other party to avoid damage or injury. Incidents may include collisions, hard braking, hard acceleration, evasive maneuvering, loss of traction, detection of objects within a threshold distance from the vehicle 108, alerts presented to the vehicle operator, component failure, inconsistent readings from sensors 120, or attempted unauthorized access to the on-board computer by external sources. Incidents may also include accidents, vehicle breakdowns, flat tires, empty fuel tanks, or medical emergencies. In some embodiments, the controller 204 may anticipate or project an expected incident based upon sensor or external data, allowing the controller 204 to send control signals to minimize the negative effects of the incident. For example, the controller 204 may cause the vehicle 108 to slow and move to the shoulder of a road immediately before running out of fuel. As another example, adjustable seats within the vehicle 108 may be adjusted to better position vehicle occupants in anticipation of a collision.
  • When an incident is determined to have occurred at block 412, information regarding the incident and the vehicle status may be recorded at block 414, either in the data storage 228 or the database 146. The information recorded at block 414 may include sensor data, communication data, and settings or configuration information prior to, during, and immediately following the incident. The information may further include a determination of whether the vehicle 108 has continued operating (either autonomously or manually) or whether the vehicle 108 is capable of continuing to operate in compliance with applicable safety and legal requirements. If the controller 204 determines that the vehicle 108 has discontinued operation or is unable to continue operation at block 416, the method 400 may terminate. If the vehicle 108 continues operation, then the method 400 may continue at block 418.
  • FIG. 4B illustrates an alternative portion of the method 400 following an incident. When an incident is determined to have occurred at block 412, the controller 204 or the server 140 may record status and operating information at block 414, as above. In some instances, the incident may interrupt communication between the vehicle 108 and the server 140 via network 130, such that not all information typically recorded will be available for recordation and analysis by the server 140. Based upon the data recorded in block 414, the server 140 or the controller 204 may determine whether assistance may be needed at the location of the vehicle 108 at block 430. For example, the controller may determine that a head-on collision has occurred based on sensor data (e.g., airbag deployment, automatic motor shut-off, LIDAR data indicating a collision, etc.) and may further determine based on information regarding the speed of the vehicle 108 and other information that medical, police, and/or towing services will be necessary. The determination that assistance is needed at block 430 may further include a determination of types of assistance needed (e.g., police, ambulance, fire, towing, vehicle maintenance, fuel delivery, etc.). The determination at block 430 may include analysis of the type of incident, the sensor data regarding the incident (e.g., images from outward facing or inward facing cameras installed within the vehicle, identification of whether any passengers were present within the vehicle, determination of whether any pedestrians or passengers in other vehicles were involved in the incident, etc.). The determination of whether assistance is needed at block 430 may further include information regarding the vehicle status determined at block 414.
  • In some embodiments, the determination at block 430 may be supplemented by a verification attempt, such as a phone call or communication through the on-board computer 114. Where the verification attempt indicates assistance is required or communication attempts fail, the server 140 or controller 204 would then determine that assistance is needed, as described above. For example, when assistance is determined to be needed at block 430 following an accident involving the vehicle 108, the server 140 may direct an automatic telephone call to a mobile telephone number associated with the vehicle 108 or the vehicle operator. If no response is received, or if the respondent indicates assistance is required, the server 140 may proceed to cause a request for assistance to be generated.
  • When assistance is determined to be needed at block 432, the controller 204 or the server 140 may send a request for assistance at block 434. The request may include information regarding the vehicle 108, such as the vehicle's location, the type of assistance required, other vehicles involved in the incident, the pedestrians involved in the incident, vehicle operators or passengers involved in the incident, and/or other relevant information. The request for assistance may include telephonic, data, or other requests to one or more emergency or vehicular service providers (e.g., local police, fire departments, state highway patrols, emergency medical services, public or private ambulance services, hospitals, towing companies, roadside assistance services, vehicle rental services, local claims representative offices, etc.). After sending a request for assistance at block 434 or when assistance is determined not to be needed at block 432, the controller 204 or the server 140 may next determine whether the vehicle is operational at block 416, as described above. The method 400 may then end or continue as indicated in FIG. 4A.
  • In some embodiments, the controller 204 may further determine information regarding the likely cause of a collision or other incident. Alternatively, or additionally, the server 140 may receive information regarding an incident from the on-board computer 114 and determine relevant additional information regarding the incident from the sensor data. For example, the sensor data may be used to determine the points of impact on the vehicle 108 and another vehicle involved in a collision, the relative velocities of each vehicle, the road conditions at the time of the incident, and the likely cause or the party likely at fault. This information may be used to determine risk levels associated with autonomous vehicle operation, as described below, even where the incident is not reported to the insurer.
  • At block 418, the controller 204 may determine whether a change or adjustment to one or more of the settings or configuration of the autonomous operation features has occurred. Changes to the settings may include enabling or disabling an autonomous operation feature or adjusting the feature's parameters (e.g., resetting the speed on an adaptive cruise control feature). If the settings or configuration are determined to have changed, the new settings or configuration may be recorded at block 422, either in the data storage 228 or the database 146.
  • At block 424, the controller 204 may record the operating data relating to the vehicle 108 in the data storage 228 or communicate the operating data to the server 140 via the network 130 for recordation in the database 146. The operating data may include the settings or configuration information, the sensor data, and the communication data discussed above, as well as data regarding control decisions generated by one or more autonomous operation features, as discussed below. In some embodiments, operating data related to normal autonomous operation of the vehicle 108 may be recorded. In other embodiments, only operating data related to incidents of interest may be recorded, and operating data related to normal operation may not be recorded. In still other embodiments, operating data may be stored in the data storage 228 until a sufficient connection to the network 130 is established, but some or all types of incident information may be transmitted to the server 140 using any available connection via the network 130.
  • At block 426, the controller 204 may determine whether the vehicle 108 is continuing to operate. In some embodiments, the method 400 may terminate when all autonomous operation features are disabled, in which case the controller 204 may determine whether any autonomous operation features remain enabled at block 426. When the vehicle 108 is determined to be operating (or operating with at least one autonomous operation feature enabled) at block 426, the method 400 may continue through blocks 406-426 until vehicle operation has ended. When the vehicle 108 is determined to have ceased operating (or is operating without autonomous operation features enabled) at block 426, the controller 204 may record the completion of operation at block 428, either in the data storage 228 or the database 146. In some embodiments, a second timestamp corresponding to the completion of vehicle operation may likewise be recorded, as above.
  • Exemplary Evaluation Methods
  • FIG. 5 illustrates a flow diagram of an exemplary autonomous operation feature evaluation method 500 for determining the effectiveness of autonomous operation features, which may be implemented by the autonomous vehicle insurance system 100. The method 500 begins by monitoring and recording the responses of an autonomous operation feature in a test environment at block 502. The test results are then used to determine a plurality of risk levels for the autonomous operation feature corresponding to the effectiveness of the feature in situations involving various conditions, configurations, and settings at block 504. Once a baseline risk profile of the plurality of risk levels has been established at block 504, the method 500 may refine or adjust the risk levels based upon operating data and actual losses for insured autonomous vehicles operation outside the test environment in blocks 506-510. Although FIG. 5 shows the method for only one autonomous operation feature, it should be understood that the method 500 may be performed to evaluate each of any number of autonomous operation features or combinations of autonomous operation features. In some embodiments, the method 500 may be implemented for a plurality of autonomous operation features concurrently on multiple servers 140 or at different times on one or more servers 140.
  • At block 502, the effectiveness of an autonomous operation feature is tested in a controlled testing environment by presenting test conditions and recording the responses of the feature. The testing environment may include a physical environment in which the autonomous operation feature is tested in one or more vehicles 108. Additionally, or alternatively, the testing environment may include a virtual environment implemented on the server 140 or another computer system in which the responses of the autonomous operation feature are simulated. Physical or virtual testing may be performed for a plurality of vehicles 108 and sensors 120 or sensor configurations, as well as for multiple settings of the autonomous operation feature. In some embodiments, the compatibility or incompatibility of the autonomous operation feature with vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, or other autonomous operation features may be tested by observing and recording the results of a plurality of combinations of these with the autonomous operation feature. For example, an autonomous operation feature may perform well in congested city traffic conditions, but that will be of little use if it is installed in an automobile with control software that operates only above 30 miles per hour. Additionally, some embodiments may further test the response of autonomous operation features or control software to attempts at unauthorized access (e.g., computer hacking attempts), which results may be used to determine the stability or reliability of the autonomous operation feature or control software.
  • The test results may be recorded by the server 140. The test results may include responses of the autonomous operation feature to the test conditions, along with configuration and setting data, which may be received by the on-board computer 114 and communicated to the server 140. During testing, the on-board computer 114 may be a special-purpose computer or a general-purpose computer configured for generating or receiving information relating to the responses of the autonomous operation feature to test scenarios. In some embodiments, additional sensors may be installed within the vehicle 108 or in the vehicle environment to provide additional information regarding the response of the autonomous operation feature to the test conditions, which additional sensors may not provide sensor data to the autonomous operation feature.
  • In some embodiments, new versions of previously tested autonomous operation features may not be separately tested, in which case the block 502 may not be present in the method 500. In such embodiments, the server 140 may determine the risk levels associated with the new version by reference to the risk profile of the previous version of the autonomous operation feature in block 504, which may be adjusted based upon actual losses and operating data in blocks 506-510. In other embodiments, each version of the autonomous operation feature may be separately tested, either physically or virtually. Alternatively, or additionally, a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version.
  • FIG. 6 illustrates a flow diagram of an exemplary autonomous operation feature testing method 600 for presenting test conditions to an autonomous operation feature and observing and recording responses to the test conditions in accordance with the method 500. Although the method 600 is illustrated for one autonomous operation feature, it should be understood that the exemplary method 600 may be performed to test any number of features or combinations of features. At block 602, the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature). The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested.
  • At block 604, the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602. The test system may be a vehicle 108 or a computer simulation, as discussed above. The autonomous operation feature or the test system may be configured to provide the desired parameter inputs to the autonomous operation feature. For example, the controller 204 may disable a number of sensors 120 or may provide only a subset of available sensor data to the autonomous operation feature for the purpose of testing the feature's response to certain parameters.
  • At block 606, test inputs are presented to the autonomous operation feature, and responses of the autonomous operation feature are observed at block 608. The test inputs may include simulated data presented by the on-board computer 114 or sensor data from the sensors 120 within the vehicle 108. In some embodiments, the vehicle 108 may be controlled within a physical test environment by the on-board computer 114 to present desired test inputs through the sensors 120. For example, the on-board computer 114 may control the vehicle 108 to maneuver near obstructions or obstacles, accelerate, or change directions to trigger responses from the autonomous operation feature. The test inputs may also include variations in the environmental conditions of the vehicle 108, such as by simulating weather conditions that may affect the performance of the autonomous operation feature (e.g., snow or ice cover on a roadway, rain, or gusting crosswinds, etc.).
  • In some embodiments, additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles. These additional vehicles may likewise be controlled by on-board computers or remotely by the server 140 through the network 130. In some embodiments, the additional vehicles may transmit autonomous communication information to the vehicle 108, which may be received by the communication component 122 or the communication unit 220 and presented to the autonomous operation feature by the on-board computer 114. Thus, the response of the autonomous operation feature may be tested with and without autonomous communications from external sources. The responses of the autonomous operation feature may be observed as output signals from the autonomous operation feature to the on-board computer 114 or the vehicle controls. Additionally, or alternatively, the responses may be observed by sensor data from the sensors 120 and additional sensors within the vehicle 108 or placed within the vehicle environment.
  • At block 610, the observed responses of the autonomous operation feature are recorded for use in determining effectiveness of the feature. The responses may be recorded in the data storage 228 of the on-board computer 114 or in the database 146 of the server 140. If the responses are stored on the on-board computer 114 during testing, the results may be communicated to the server 140 via the network either during or after completion of testing.
  • At block 612, the on-board computer 114 or the server 140 may determine whether the additional sets of parameters remain for which the autonomous operation feature is to be tested, as determined in block 602. When additional parameter sets are determined to remain at block 612, they are separately tested according to blocks 604-610. When no additional parameter sets are determined to exist at block 612, the method 600 terminates.
  • Although the method 600 is discussed above as testing the autonomous (and/or semi-autonomous) operation features in a test vehicle operating within a test environment, it should be understood that the exemplary method 600 may be similarly performed in an uncontrolled environment (i.e., on public roadways) or in a virtual environment. Testing of autonomous features within a virtual environment may include the presentation of electrical signals mimicking signals generated by one or more sensors in a plurality of operating scenarios at block 606. For example, a control unit or on-board computer 114 removed from a vehicle may be connected to one or more sensor input simulators (e.g., a computer or computer-controlled signal generator) that present input signals to the control unit or on-board computer that correspond to signals that would be received from the sensors 120 in the vehicle 108 under certain conditions. In such case, the same or another computer may be connected to the control unit or on-board computer 114 to receive and record the control outputs determined by the one or more autonomous operation features in response to the simulated sensor input at blocks 608 and 610.
  • Additionally, or alternatively, the virtual test environment may include a simulation of an autonomous (and/or semi-autonomous) operation feature running on a general-purpose or special-purpose computer system. In such an embodiment, the autonomous operation feature may include one or more software routines, processes, applications, or modules that implement the autonomous operation feature to generate control signals for a vehicle when executed on a general-purpose or special-purpose processor. For example, an adaptive cruise control feature may include a software routine to monitor the speed of the vehicle using a combination of speedometer and other sensor data, as well as a software routine to determine the distance of the vehicle from obstacles or other vehicles in the vehicle's path using LIDAR and autonomous communication data. The adaptive cruise control feature may further include a control software routine to determine adjustments to the vehicle's speed to maintain a safe distance from other vehicles and obstacles then generate a control signal to maintain or adjust the throttle of the vehicle. In a virtual test environment, the software routines of the autonomous operation feature may be executed on a processor of a computer system not connected to any vehicle, in which case test input signals simulating signals from sensors within a vehicle may be presented to the software routines to test the routines' responses. Thus, a process of the computer system may execute instructions causing the processor to access a set of simulated sensor test input signals at block 606, determine a response (such as one or more output test signals) of the autonomous operation feature based on the executable instructions representing one or more software routines of the autonomous operation feature at block 608, and record the determined response at block 610.
  • As the software routines of the autonomous operation feature are not directly connected to a vehicle to control the vehicle's operation during virtual testing, virtual testing may further include predicting a response of a vehicle to output test signals generated by the software routines of the tested autonomous operation feature. The simulated sensor inputs and/or test input signals may include sets of data corresponding to generated or recorded signals from a plurality of sensors. In some embodiments, the computer system may access a first set of sensor data and sequentially update the sensor data using the determined responses of the simulated autonomous operation feature. Thus, the test input signals may include input signals stored in a database and accessed by a testing program or application for presentation to the software routines of the autonomous operation feature being tested. Additionally, or alternatively, the computer system may generate sensor input signals based on a simulation of a physical test environment and update the virtual test environment based on the determined responses of the autonomous operation features. In some embodiments, a virtual testing program or application may further control the virtual testing process and may simulate the operation of a virtual vehicle within a virtual test environment. The virtual test environment may include a computer simulation of an environment in which an autonomous vehicle may be expected to operate (e.g., a congested highway, a city street with multiple intersections, etc.). The virtual testing program or application may, thus, generate a simulation of the operation of the virtual vehicle in the virtual test environment, including vehicle attributes such as position, speed, or momentum. The virtual testing program may further simulate the presence or operation of other vehicles, traffic control devices, obstacles, pedestrians, or other relevant features of a vehicle environment. Based upon these simulated features of the virtual vehicle environment, one or more simulated sensor readings or data may be determined, which may further be used to determine one or more test input signals to the software routines of the autonomous operation feature. The virtual testing program or application may then present the test input signals to the software routines and cause test output signals to be generated in response to the test input signals. From these test output signals, the virtual testing program may then predict the response of the virtual vehicle to the test output signals, including the responses of the virtual vehicle in relation to other virtual vehicles or other features in the simulated virtual test environment.
  • In any of the foregoing virtual test environments, the input data may include sensor data recorded during operation of an autonomous (and/or semi-autonomous) vehicle 108, which may include operation by a vehicle operator or by other autonomous (and/or semi-autonomous) operation features. For example, a vehicle operator may choose to operate the vehicle 108 manually under some conditions (e.g., snow, fog, or construction), or the autonomous operation features may not support autonomous operation under such conditions. The sensors 120 of the vehicle 108 may, however, continue to collect and record data regarding the surrounding environment. The sensor data may then be used to simulate autonomous operation feature responses (i.e., the control signals the autonomous operation feature would have generated had it been in control of the vehicle). The data and responses may be stored or transmitted via the network 130, and the responses of the autonomous operation features may be determined at the time of operation or at a later time.
  • As discussed with reference to FIG. 5 and elsewhere herein, the effectiveness of one or more autonomous operation features may be further used to determine one or more risk levels or risk profiles associated with the autonomous operation features. Specifically, the test output responses generated by the software routines of the autonomous operation features or the predicted responses of the virtual vehicle to the test output responses may be compared with other similar virtual test data related to other autonomous operation features. Where available, actual observed operating data regarding the one or more other autonomous operation features disposed within a plurality of other vehicles operating outside the virtual test environment may also be compared with the virtual test data using known statistical modeling techniques. Additionally, actual loss data for vehicles operating outside the virtual test environment and having the other autonomous operating features may further be compared with the virtual test data to better assess the risk levels and risk profiles associated with the tested autonomous operating features.
  • It will be apparent that performance of the exemplary method 600 in a virtual test environment offers advantages in terms of cost and time. Once set up, hundreds or thousands of test scenarios may be automatically run to evaluate autonomous (and/or semi-autonomous) operation feature performance under a variety of conditions without input from a user or vehicle operator. For example, a new autonomous operation feature or a software update including a new version of an autonomous operation feature may be tested in a virtual test environment prior to installation within autonomous vehicles, allowing immediate adjustment of risk levels or risk profiles for vehicles using the new autonomous operation feature or version. In this way, adjustments to risks associated with autonomous operation features may be made without reference to actual loss data relating to the specific autonomous operation features. Such advantages must be weighed against the limitations of virtual testing, however, because the test results are limited by the quality of the virtual test environment. It will be readily apparent that responses from physical and virtual test environments may be combined in order to determine the performance and risk levels associated with autonomous operation features.
  • Referring again to FIG. 5, the server 140 may determine a baseline risk profile for the autonomous operation feature from the recorded test results at block 504, including a plurality of risk levels corresponding to a plurality of sets of parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these. The server 140 may determine the risk levels associated with the autonomous operation feature by implementing the feature evaluation application 142 to determine the effectiveness of the feature. In some embodiments, the server 140 may further implement the compatibility evaluation application 143 to determine the effectiveness of combinations of features based upon test results and other information. Additionally, or alternatively, in some embodiments, the baseline risk profile may not depend upon the type, make, model, year, or other aspect of the vehicle 108. In such embodiments, the baseline risk profile and adjusted risk profiles may correspond to the effectiveness or risk levels associated with the autonomous operation features across a range of vehicles, disregarding any variations in effectiveness or risk levels associated with operation of the features in different vehicles.
  • FIG. 7 illustrates a flow diagram of an exemplary autonomous feature evaluation method 700 for determining the effectiveness of an autonomous operation feature under a set of environmental conditions, configuration conditions, and settings. Although the method 700 shows determination of a risk level associated with an autonomous operation feature within one set of parameters, it should be understood that the method 700 may be implemented for any number of sets of parameters for any number of autonomous features or combinations thereof.
  • At block 702, the server 140 receives the test result data observed and recorded in block 502 for the autonomous operation feature in conjunction with a set of parameters. In some embodiments, the rest result data may be received from the on-board computer 114 or from the database 146. In addition, in some embodiments, the server 140 may receive reference data for other autonomous operation features in use on insured autonomous vehicles at block 704, such as test result data and corresponding actual loss or operating data for the other autonomous operation features. The reference data received at block 704 may be limited to data for other autonomous operation features having sufficient similarity to the autonomous operation feature being evaluated, such as those performing a similar function, those with similar test result data, or those meeting a minimum threshold level of actual loss or operating data.
  • Using the test result data received at block 702 and the reference data received at block 704, the server 140 determines the expected actual loss or operating data for the autonomous operation feature at block 706. The server 140 may determine the expected actual loss or operating data using known techniques, such as regression analysis or machine learning tools (e.g., neural network algorithms or support vector machines). The expected actual loss or operating data may be determined using any useful metrics, such as expected loss value, expected probabilities of a plurality of collisions or other incidents, expected collisions per unit time or distance traveled by the vehicle, etc.
  • At block 708, the server 140 may further determine a risk level associated with the autonomous operation feature in conjunction with the set of parameters received in block 702. The risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning. The risk level may be defined in various alternative ways, including as a probability of loss per unit time or distance traveled, a percentage of collisions avoided, or a score on a fixed scale. In a preferred embodiment, the risk level is defined as an effectiveness rating score such that a higher score corresponds to a lower risk of loss associated with the autonomous operation feature.
  • Referring again to FIG. 5, the method 700 may be implemented for each relevant combination of an autonomous operation feature in conjunction with a set of parameters relating to environmental conditions, configuration conditions, and settings. It may be beneficial in some embodiments to align the expected losses or operating data metrics with loss categories for vehicle insurance policies. Once the baseline risk profile is determined for the autonomous operation feature, the plurality of risk levels in the risk profile may be updated or adjusted in blocks 506-510 using actual loss and operating data from autonomous vehicles operating in the ordinary course, viz. not in a test environment.
  • At block 506, the server 140 may receive operating data from one or more vehicles 108 via the network 130 regarding operation of the autonomous operation feature. The operating data may include the operating data discussed above with respect to monitoring method 400, including information regarding the vehicle 108, the vehicle's environment, the sensors 120, communications for external sources, the type and version of the autonomous operation feature, the operation of the feature, the configuration and settings relating to the operation of the feature, the operation of other autonomous operation features, control actions performed by the vehicle operator, or the location and time of operation. The operating data may be received by the server 140 from the on-board computer 114 or the mobile device 110 implementing the monitoring method 400 or from other sources, and the server 140 may receive the operating data either periodically or continually.
  • At block 508, the server 140 may receive data regarding actual losses on autonomous vehicles that included the autonomous operation feature. This information may include claims filed pursuant to insurance policies, claims paid pursuant to insurance policies, accident reports filed with government agencies, or data from the sensors 120 regarding incidents (e.g., collisions, alerts presented, etc.). This actual loss information may further include details such as date, time, location, traffic conditions, weather conditions, road conditions, vehicle speed, vehicle heading, vehicle operating status, autonomous operation feature configuration and settings, autonomous communications transmitted or received, points of contact in a collision, velocity and movements of other vehicles, or additional information relevant to determining the circumstances involved in the actual loss.
  • At block 510, the server 140 may process the information received at blocks 506 and 508 to determine adjustments to the risk levels determined at block 504 based upon actual loss and operating data for the autonomous operation feature. Adjustments may be necessary because of factors such as sensor failure, interference disrupting autonomous communication, better or worse than expected performance in heavy traffic conditions, etc. The adjustments to the risk levels may be made by methods similar to those used to determine the baseline risk profile for the autonomous operation feature or by other known methods (e.g., Bayesian updating algorithms). The updating procedure of blocks 506-510 may be repeatedly implemented periodically or continually as new data become available to refine and update the risk levels or risk profile associated with the autonomous operation feature. In subsequent iterations, the most recently updated risk profile or risk levels may be adjusted, rather than the initial baseline risk profile or risk levels determined in block 504.
  • Exemplary Autonomous Vehicle Insurance Risk and Price Determination Methods
  • The risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles. FIGS. 8-10 illustrate flow diagrams of exemplary embodiments of methods for determining risk associated with an autonomous vehicle or premiums for vehicle insurance policies covering an autonomous vehicle. In some embodiments or under some conditions, the autonomous vehicle may be a fully autonomous vehicle operating without a vehicle operator's input or presence. In other embodiments or under other conditions, the vehicle operator may control the vehicle with or without the assistance of the vehicle's autonomous operation features. For example, the vehicle may be fully autonomous only above a minimum speed threshold or may require the vehicle operator to control the vehicle during periods of heavy precipitation. Alternatively, the autonomous vehicle may perform all relevant control functions using the autonomous operation features under all ordinary operating conditions. In still further embodiments, the vehicle 108 may operate in either a fully or a partially autonomous state, while receiving or transmitting autonomous communications.
  • Where the vehicle 108 operates only under fully autonomous control by the autonomous operation features under ordinary operating conditions or where control by a vehicle operator may be disregarded for insurance risk and price determination, the method 800 may be implemented to determine the risk level or premium associated with an insurance policy covering the autonomous vehicle. Where the vehicle 108 may be operated manually under some conditions, the method 900 may be implemented to determine the risk level or premium associated with an insurance policy covering the autonomous vehicle, including a determination of the risks associated with the vehicle operator performing manual vehicle operation. Where the vehicle 108 may be operated with the assistance of autonomous communications features, the method 1000 may be implemented to determine the risk level or premium associated with an insurance policy covering the autonomous vehicle, including a determination of the expected use of autonomous communication features by external sources in the relevant environment of the vehicle 108 during operation of the vehicle 108.
  • FIG. 8 illustrates a flow diagram depicting an exemplary embodiment of a fully autonomous vehicle insurance pricing method 800, which may be implemented by the autonomous vehicle insurance system 100. The method 800 may be implemented by the server 140 to determine a risk level or price for a vehicle insurance policy covering a fully autonomous vehicle based upon the risk profiles of the autonomous operation features in the vehicle. It is important to note that the risk category or price is determined without reference to factors relating to risks associated with a vehicle operator (e.g., age, experience, prior history of vehicle operation). Instead, the risk and price may be determined based upon the vehicle 108, the location and use of the vehicle 108, and the autonomous operation features of the vehicle 108.
  • At block 802, the server 140 receives a request to determine a risk category or premium associated with a vehicle insurance policy for a fully autonomous vehicle. The request may be caused by a vehicle operator or other customer or potential customer of an insurer, or by an insurance broker or agent. The request may also be generated automatically (e.g., periodically for repricing or renewal of an existing vehicle insurance policy). In some instances, the server 140 may generate the request upon the occurrence of specified conditions.
  • At block 804, the server 140 receives information regarding the vehicle 108, the autonomous operation features installed within the vehicle 108, and anticipated or past use of the vehicle 108. The information may include vehicle information (e.g., type, make, model, year of production, safety features, modifications, installed sensors, on-board computer information, etc.), autonomous operation features (e.g., type, version, connected sensors, compatibility information, etc.), and use information (e.g., primary storage location, primary use, primary operating time, past use as monitored by an on-board computer or mobile device, past use of one or more vehicle operators of other vehicles, etc.). The information may be provided by a person having an interest in the vehicle, a customer, or a vehicle operator, and/or the information may be provided in response to a request for the information by the server 140. Alternatively, or additionally, the server 140 may request or receive the information from one or more databases communicatively connected to the server 140 through the network 130, which may include databases maintained by third parties (e.g., vehicle manufacturers or autonomous operation feature manufacturers). In some embodiments, information regarding the vehicle 108 may be excluded, in which case the risk or premium determinations below may likewise exclude the information regarding the vehicle 108.
  • At block 806, the server 140 may determine the risk profile or risk levels associated with the vehicle 108 based upon the vehicle information and the autonomous operation feature information received at block 804. The risk levels associated with the vehicle 108 may be determined as discussed above with respect to the method 500 and/or may be determined by looking up in a database the risk level information previously determined. In some embodiments, the information regarding the vehicle may be given little or no weight in determining the risk levels. In other embodiments, the risk levels may be determined based upon a combination of the vehicle information and the autonomous operation information. As with the risk levels associated with the autonomous operation features discussed above, the risk levels associated with the vehicle may correspond to the expected losses or incidents for the vehicle based upon its autonomous operation features, configuration, settings, and/or environmental conditions of operation. For example, a vehicle may have a risk level of 98% effectiveness when on highways during fair weather days and a risk level of 87% effectiveness when operating on city streets at night in moderate rain. A plurality of risk levels associated with the vehicle may be combined with estimates of anticipated vehicle use conditions to determine the total risk associated with the vehicle.
  • At block 808, the server 140 may determine the expected use of the vehicle 108 in the relevant conditions or with the relevant settings to facilitate determining a total risk for the vehicle 108. The server 140 may determine expected vehicle use based upon the use information received at block 804, which may include a history of prior use recorded by the vehicle 108 and/or another vehicle. For example, recorded vehicle use information may indicate that 800/% of vehicle use occurs during weekday rush hours in or near a large city, that 20% occurs on nights and weekends. From this information, the server 140 may determine that 80% (75%, 90%, etc.) of the expected use of the vehicle 108 is in heavy traffic and that 20% (25%, 10%, etc.) is in light traffic. The server 140 may further determine that vehicle use is expected to be 60% on limited access highways and 40% on surface streets. Based upon the vehicle's typical storage location, the server 140 may access weather data for the location to determine expected weather conditions during the relevant times. For example, the server 140 may determine that 20% of the vehicle's operation on surface streets in heavy traffic will occur in rain or snow. In a similar manner, the server 140 may determine a plurality of sets of expected vehicle use parameters corresponding to the conditions of use of the vehicle 108. These conditions may further correspond to situations in which different autonomous operation features may be engaged and/or may be controlling the vehicle. Additionally, or alternatively, the vehicle use parameters may correspond to different risk levels associated with the autonomous operation features. In some embodiments, the expected vehicle use parameters may be matched to the most relevant vehicle risk level parameters, viz. the parameters corresponding to vehicle risk levels that have the greatest predictive effect and/or explanatory power.
  • At block 810, the server 140 may use the risk levels determined at block 806 and the expected vehicle use levels determined at block 808 to determine a total expected risk level. To this end, it may be advantageous to attempt to match the vehicle use parameters as closely as possible to the vehicle risk level parameters. For example, the server 140 may determine the risk level associated with each of a plurality of sets of expected vehicle use parameters. In some embodiments, sets of vehicle use parameters corresponding to zero or negligible (e.g., below a predetermined threshold probability) expected use levels may be excluded from the determination for computational efficiency. The server 140 may then weight the risk levels by the corresponding expected vehicle use levels, and aggregate the weighted risk levels to obtain a total risk level for the vehicle 108. In some embodiments, the aggregated weighted risk levels may be adjusted or normalized to obtain the total risk level for the vehicle 108. In some embodiments, the total risk level may correspond to a regulatory risk category or class of a relevant insurance regulator.
  • At block 812, the server 140 may determine one or more premiums for vehicle insurance policies covering the vehicle 108 based upon the total risk level determined at block 810. These policy premiums may also be determine based upon additional factors, such as coverage type and/or amount, expected cost to repair or replace the vehicle 108, expected cost per claim for liability in the locations where the vehicle 108 is typically used, discounts for other insurance coverage with the same insurer, and/or other factors unrelated to the vehicle operator. In some embodiments, the server 140 may further communicate the one or more policy premiums to a customer, broker, agent, or other requesting person or organization via the network 130. The server 140 may further store the one or more premiums in the database 146.
  • FIG. 9 illustrates a flow diagram depicting an exemplary embodiment of a partially autonomous vehicle insurance pricing method 900, which may be implemented by the autonomous vehicle insurance system 100 in a manner similar to that of the method 800. The method 900 may be implemented by the server 140 to determine a risk category and/or price for a vehicle insurance policy covering an autonomous vehicle based upon the risk profiles of the autonomous operation features in the vehicle and/or the expected use of the autonomous operation features. In addition to information regarding the vehicle 108 and the autonomous operation features, the method 900 includes information regarding the vehicle operator, including information regarding the expected use of the autonomous operation features and/or the expected settings of the features under various conditions. Such additional information is relevant where the vehicle operator may control the vehicle 108 under some conditions and/or may determine settings affecting the effectiveness of the autonomous operation features.
  • At block 902, the server 140 may receive a request to determine a risk category and/or premium associated with a vehicle insurance policy for an autonomous vehicle in a manner similar to block 802 described above. At block 904, the server 140 likewise receives information regarding the vehicle 108, the autonomous operation features installed within the vehicle 108, and/or anticipated or past use of the vehicle 108. The information regarding anticipated or past use of the vehicle 108 may include information regarding past use of one or more autonomous operation features, and/or settings associated with use of the features. For example, this may include times, road conditions, and/or weather conditions when autonomous operation features have been used, as well as similar information for past vehicle operation when the features have been disabled. In some embodiments, information regarding the vehicle 108 may be excluded, in which case the risk or premium determinations below may likewise exclude the information regarding the vehicle 108. At block 906, the server 140 may receive information related to the vehicle operator, including standard information of a type typically used in actuarial analysis of vehicle operator risk (e.g., age, location, years of vehicle operation experience, and/or vehicle operating history of the vehicle operator).
  • At block 908, the server 140 may determine the risk profile or risk levels associated with the vehicle 108 based upon the vehicle information and the autonomous operation feature information received at block 904. The risk levels associated with the vehicle 108 may be determined as discussed above with respect to the method 500 and/or as further discussed with respect to method 800.
  • At block 910, the server 140 may determine the expected manual and/or autonomous use of the vehicle 108 in the relevant conditions and/or with the relevant settings to facilitate determining a total risk for the vehicle 108. The server 140 may determine expected vehicle use based upon the use information received at block 904, which may include a history of prior use recorded by the vehicle 108 and/or another vehicle for the vehicle operator. Expected manual and autonomous use of the vehicle 108 may be determined in a manner similar to that discussed above with respect to method 800, but including an additional determination of the likelihood of autonomous and/or manual operation by the vehicle operation under the various conditions. For example, the server 140 may determine based upon past operating data that the vehicle operator manually controls the vehicle 108 when on a limited-access highway only 20% of the time in all relevant environments, but the same vehicle operator controls the vehicle 60% of the time on surface streets outside of weekday rush hours and 35% of the time on surface streets during weekday rush hours. These determinations may be used to further determine the total risk associated with both manual and/or autonomous vehicle operation.
  • At block 912, the server 140 may use the risk levels determined at block 908 and the expected vehicle use levels determined at block 910 to determine a total expected risk level, including both manual and autonomous operation of the vehicle 108. The autonomous operation risk levels may be determined as above with respect to block 810. The manual operation risk levels may be determined in a similar manner, but the manual operation risk may include risk factors related to the vehicle operator. In some embodiments, the manual operation risk may also be determined based upon vehicle use parameters and/or related autonomous operation feature risk levels for features that assist the vehicle operator in safely controlling the vehicle. Such features may include alerts, warnings, automatic braking for collision avoidance, and/or similar features that may provide information to the vehicle operator or take control of the vehicle from the vehicle operator under some conditions. These autonomous operation features may likewise be associated with different risk levels that depend upon settings selected by the vehicle operator. Once the risk levels associated with autonomous operation and manual operation under various parameter sets that have been weighted by the expected use levels, the total risk level for the vehicle and operator may be determined by aggregating the weighted risk levels. As above, the total risk level may be adjusted or normalized, and/or it may be used to determine a risk category or risk class in accordance with regulatory requirements.
  • At block 914, the server 140 may determine one or more premiums for vehicle insurance policies covering the vehicle 108 based upon the total risk level determined at block 812. As in method 800, additional factors may be included in the determination of the policy premiums, and/or the premiums may be adjusted based upon additional factors. The server 140 may further record the premiums or may transmit one or more of the policy premiums to relevant parties.
  • FIG. 10 illustrates a flow diagram depicting an exemplary embodiment of an autonomous vehicle insurance pricing method 1000 for determining risk and/or premiums for vehicle insurance policies covering autonomous vehicles with autonomous communication features, which may be implemented by the autonomous vehicle insurance system 100. The method 1000 may determine risk levels as without autonomous communication discussed above with reference to methods 800 and/or 900, then adjust the risk levels based upon the availability and effectiveness of communications between the vehicle 108 and external sources. Similar to environmental conditions, the availability of external sources such as other autonomous vehicles for communication with the vehicle 108 affects the risk levels associated with the vehicle 108. For example, use of an autonomous communication feature may significantly reduce risk associated with autonomous operation of the vehicle 108 only where other autonomous vehicles also use autonomous communication features to send and/or receive information.
  • At block 1002, the server 140 may receive a request to determine a risk category or premium associated with a vehicle insurance policy for an autonomous vehicle with one or more autonomous communication features in a manner similar to blocks 802 and/or 902 described above. At block 1004, the server 140 likewise receives information regarding the vehicle 108, the autonomous operation features installed within the vehicle 108 (including autonomous communication features), the vehicle operator, and/or anticipated or past use of the vehicle 108. The information regarding anticipated or past use of the vehicle 108 may include information regarding locations and times of past use, as well as past use of one or more autonomous communication features. For example, this may include locations, times, and/or details of communication exchanged by an autonomous communication feature, as well as information regarding past vehicle operation when no autonomous communication occurred. This information may be used to determine the past availability of external sources for autonomous communication with the vehicle 108, facilitating determination of expected future availability of autonomous communication as described below. In some embodiments, information regarding the vehicle 108 may be excluded, in which case the risk or premium determinations below may likewise exclude the information regarding the vehicle 108.
  • At block 1006, the server 140 may determine the risk profile or risk levels associated with the vehicle 108 based upon the vehicle information, the autonomous operation feature information, and/or the vehicle operator information received at block 1004. The risk levels associated with the vehicle 108 may be determined as discussed above with respect to the method 500 and as further discussed with respect to methods 800 and 900. At block 1008, the server 140 may determine the risk profile and/or risk levels associated with the vehicle 108 and/or the autonomous communication features. This may include a plurality of risk levels associated with a plurality of autonomous communication levels and/or other parameters relating to the vehicle 108, the vehicle operator, the autonomous operation features, the configuration and/or setting of the autonomous operation features, and/or the vehicle's environment. The autonomous communication levels may include information regarding the proportion of vehicles in the vehicle's environment that are in autonomous communication with the vehicle 108, levels of communication with infrastructure, types of communication (e.g., hard braking alerts, full velocity information, etc.), and/or other information relating to the frequency and/or quality of autonomous communications between the autonomous communication feature and the external sources.
  • At block 1010, the server 140 may then determine the expected use levels of the vehicle 108 in the relevant conditions, autonomous operation feature settings, and/or autonomous communication levels to facilitate determining a total risk for the vehicle 108. The server 140 may determine expected vehicle use based upon the use information received at block 1004, including expected levels of autonomous communication under a plurality of sets of parameters. For example, the server 140 may determine based upon past operating data that the 50% of the total operating time of the vehicle 108 is likely to occur in conditions where approximately a quarter of the vehicles utilize autonomous communication features, 40% of the total operating time is likely to occur in conditions where a negligible number of vehicles utilize autonomous communication features, and/or 10% is likely to occur in conditions where approximately half of vehicles utilize autonomous communication features. Of course, each of the categories in the preceding example may be further divided by other conditions, such as traffic levels, weather, average vehicle speed, presence of pedestrians, location, autonomous operation feature settings, and/or other parameters. These determinations may be used to further determine the total risk associated with autonomous vehicle operation including autonomous communication.
  • At block 1012, the server 140 may use the risk levels determined at block 1010 to determine a total expected risk level for the vehicle 108 including one or more autonomous communication features, in a similar manner to the determination described above in block 810. The server 140 may weight each of the risk levels corresponding to sets of parameters by the expected use levels corresponding to the same set of parameters. The weighted risk levels may then be aggregated using known techniques to determine the total risk level. As above, the total risk level may be adjusted or normalized, or it may be used to determine a risk category or risk class in accordance with regulatory requirements.
  • At block 1014, the server 140 may determine one or more premiums for vehicle insurance policies covering the vehicle 108 based upon the total risk level determined at block 1012. As in methods 800 and/or 900, additional factors may be included in the determination of the policy premiums, and/or the premiums may be adjusted based upon additional factors. The server 140 may further record the premiums and/or may transmit one or more of the policy premiums to relevant parties.
  • In any of the preceding embodiments, the determined risk level or premium associated with one or more insurance policies may be presented by the server 140 to a customer or potential customer as offers for one or more vehicle insurance policies. The customer may view the offered vehicle insurance policies on a display such as the display 202 of the mobile device 110, select one or more options, and/or purchase one or more of the vehicle insurance policies. The display, selection, and/or purchase of the one or more policies may be facilitated by the server 140, which may communicate via the network 130 with the mobile device 110 and/or another computer device accessed by the user.
  • Additionally, or alternatively, any of the preceding embodiments may determine or adjust vehicle insurance coverage types or levels (e.g., deductibles, coverage amounts, etc.) based upon use conditions and/or autonomous (and/or semi-autonomous) operation feature use, configuration, or settings. For example, deductibles or premiums for a level of vehicle insurance coverage for theft of a vehicle may be reduced for policies where a fully autonomous vehicle includes autonomous operation features capable of returning the vehicle if stolen. As another example, coverage levels of a vehicle insurance policy may vary based upon whether an autonomous vehicle contains any passengers or vehicle operators. Additionally, coverage types or levels may be adjusted based upon use levels of the autonomous operation features and/or information regarding a vehicle operator. For example, certain coverage types or levels may be unavailable to a vehicle operator (e.g., inexperienced drivers, drivers with extensive accident histories, etc.), except that such coverage may be available when certain autonomous operation features are enabled or activated. In some embodiments, vehicle operators who may be otherwise unable or legally prevented from operating a vehicle (e.g., due to blindness, physical disabilities, revocation of an operating license, etc.) may be insured for operation of an autonomous vehicle with certain autonomous operation features enabled.
  • Exemplary Methods of Providing Insurance Coverage
  • In one aspect, a computer-implemented method of adjusting or creating an insurance policy may be provided. The method may include: (1) capturing or gathering data, via a processor, to determine an autonomous or semi-autonomous technology or functionality associated with a specific vehicle; (2) comparing the received data, via the processor, to a stored baseline of vehicle data created from (a) actual accident data involving automobiles equipped with the autonomous or semi-autonomous technology or functionality, and/or (b) autonomous or semi-autonomous vehicle testing; (3) identifying (or assessing) accident or collision risk, via the processor, based upon an ability of the autonomous or semi-autonomous technology or functionality associated with the specific vehicle to make driving decisions and/or avoid or mitigate crashes; (4) adjusting or creating an insurance policy, via the processor, based upon the accident or collision risk identified that is based upon the ability of the autonomous or semi-autonomous technology or functionality associated with the specific vehicle; and/or (5) presenting on a display screen, or otherwise providing or communicating, all or a portion of (such as a monthly premium or discount) the insurance policy adjusted or created to a potential or existing customer, or an owner or operator of the specific vehicle equipped with the autonomous or semi-autonomous technology or functionality, for review, acceptance, and/or approval. The method may include additional, fewer, or alternative steps or actions, including those discussed elsewhere herein.
  • For instance, the method may include evaluating, via the processor, an effectiveness of the autonomous or semi-autonomous technology or functionality, and/or an associated artificial intelligence, in a test environment, and/or using real driving experience or information.
  • The identification (or assessment) of accident or collision risk performed by the processor may be dependent upon the extent of control and/or decision making that is assumed by the specific vehicle equipped with the autonomous or semi-autonomous technology or functionality, rather than the human driver. Additionally or alternatively, the identification (or assessment) of accident or collision risk may be dependent upon (a) the ability of the specific vehicle to use external information (such as vehicle-to-vehicle, vehicle-to-infrastructure, and/or infrastructure-to-vehicle wireless communication) to make driving decisions, and/or (b) the availability of such external information, such as may be determined by a geographical region (urban or rural) associated with the specific vehicle or vehicle owner.
  • Information regarding the autonomous or semi-autonomous technology or functionality associated with the specific vehicle, including factory-installed hardware and/or versions of computer instructions, may be wirelessly transmitted to a remote server associated with an insurance provider and/or other third party for analysis. The method may include remotely monitoring an amount or percentage of usage of the autonomous or semi-autonomous technology or functionality by the specific vehicle, and based upon such amount or percentage of usage, (a) providing feedback to the driver and/or insurance provider via wireless communication, and/or (b) adjusting insurance policies or premiums.
  • Data Acquisition
  • In one aspect, the present embodiments may relate to data acquisition. Data may be gathered via devices employing wireless communication technology, such as Bluetooth or other IEEE communication standards. In one embodiment, a Bluetooth enabled smartphone or mobile device, and/or an in-dash smart and/or communications device may collect data. The data associated with the vehicle, and/or vehicle or driver performance, that is gathered or collected at, or on, the vehicle may be wirelessly transmitted to a remote processor or server, such as a remote processor or server associated with an insurance provider. The mobile device 110 may receive the data from the on-board computer 114 or the sensors 120, and may transmit the received data to the server 140 via the network 130, and the data may be stored in the database 146. In some embodiments, the transmitted data may include real-time sensor data, a summary of the sensor data, processed sensor data, operating data, environmental data, communication data, or a log such data.
  • A. Vehicle Decision Making
  • Data may be generated by autonomous or semi-autonomous vehicles and/or vehicle mounted sensors (or smart sensors), and then collected by vehicle mounted equipment or processors, including Bluetooth devices, and/or an insurance provider remote processor or server. The data gathered may be used to analyze vehicle decision making. A processor may be configured to generate data on what an autonomous or semi-autonomous vehicle would have done in a given situation had the driver not taken over manual control/driving of the vehicle or alternative control actions not taken by the autonomous or semi-autonomous operation features. This type of control decision data (related to vehicle decision making) may be useful with respect to analyzing hypothetical situations.
  • In one embodiment, an application, or other computer or processor instructions, may interact with a vehicle to receive and/or retrieve data from autonomous or semi-autonomous processors and sensors. The data retrieved may be related to radar, cameras, sensor output, computer instructions or application output. Other data related to a smart vehicle controller, car navigation unit information (including route history information and typical routes taken), GPS unit information, odometer and/or speedometer information, and smart equipment data may also be gathered or collected. The application and/or other computer instructions may be associated with an insurance provider remote processor or server.
  • The control decision data may further include information regarding control decisions generated by one or more autonomous operation features within the vehicle. The operating data and control decision data gathered, collected, and/or acquired may facilitate remote evaluation and/or analysis of what the autonomous or semi-autonomous vehicle was “trying to do” (brake, slow, turn, accelerate, etc.) during operation, as well as what the vehicle actually did do. The data may reveal decisions, and the appropriateness thereof, made by the artificial intelligence or computer instructions associated with one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment. The data may include information related to what the vehicle would have done in a situation if the driver had not taken over (beginning manual vehicle control). Such data may include both the control actions taken by the vehicle and control actions the autonomous or semi-autonomous operation features would have caused the vehicle to take. Thus, in some embodiments, the control decisions data may include information regarding control decisions not implemented by the autonomous operation features to control the vehicle. This may occur when an autonomous operation feature generates a control decision or associated control signal, but the control decision or signal is prevented from controlling the vehicle because the autonomous feature or function is disabled, the control decision is overridden by the vehicle operator, the control signal would conflict with another control signal generated by another autonomous operation feature, a more preferred control decision is generated, or an error occurs in the on-board computer 114 or the control system of the vehicle.
  • For example, a vehicle operator may disable or constrain the operation of some or all autonomous operation features, such as where the vehicle is operated manually or semi-autonomously. The disabled or constrained autonomous operation features may, however, continue to receive sensor data and generate control decision data that is not implemented. Similarly, one or more autonomous operation features may generate more than one control decision in a relevant period of time as alternative control decisions. Some of these alternative control decisions may not be selected by the autonomous operation feature or an autonomous operation control system to control the vehicle. For example, such alternative control decisions may be generated based on different sets of sensor or communication data from different sensors 120 or include or excluding autonomous communication data. As another example, the alternative control decisions may be generated faster than they can be implemented by the control system of the vehicle, thus preventing all control decisions from being implemented.
  • In addition to control decision data, other information regarding the vehicle, the vehicle environment, or vehicle operation may be collected, generated, transmitted, received, requested, stored, or recorded in connection with the control decision data. As discussed elsewhere herein, additional operating data including sensor data from the sensors 120, autonomous communication data from the communication component 122 or the communication module 220, location data, environmental data, time data, settings data, configuration data, and/or other relevant data may be associated with the control decision data. In some embodiments, a database or log may store the control decision data and associated information. In further embodiments, the entries in such log or database may include a timestamp indicating the date, time, location, vehicle environment, vehicle condition, autonomous operation feature settings, and/or autonomous operation feature configuration information associated with each entry. Such data may facilitate evaluating the autonomous or semi-autonomous technology, functionality, system, and/or equipment in hypothetical situations and/or may be used to calculate risk, and in turn adjust insurance policies, premiums, discounts, etc.
  • B. Evaluating Risk
  • The data gathered may be used to evaluate risk associated with the autonomous or semi-autonomous operation feature or technology at issue. As discussed elsewhere herein, information regarding the operation of the vehicle may be monitored or associated with test data or actual loss data regarding losses associated with insurance policies for other vehicles having the autonomous technology or feature to determine risk levels and/or risk profiles. Specifically, the control decision data, sensor data, and other operating data discussed above may be used to determine risk levels, loss models, and/or risk profiles associated with one or more autonomous or semi-autonomous operation features. External data may further be used to determine risk, as discussed below. Such determined risk levels may further be used to determine insurance rates, premiums, discounts, or costs as discussed in greater detail below.
  • In one embodiment, the data gathered may be used to determine an average distance to another vehicle ahead of, and/or behind, the vehicle during normal use of the autonomous or semi-autonomous vehicle technology, functionality, system, and/or equipment. A safe driving distance to other vehicles on the road may lower the risk of accident.
  • The data gathered may also relate to how quickly the technology, functionality, system, and/or equipment may properly stop or slow a vehicle in response to a light changing from green to yellow, and/or from yellow to red. Timely stopping at traffic lights may also positively impact risk of collision.
  • The data gathered may indicate issues not entirely related to the autonomous or semi-autonomous technology, functionality, system, and/or equipment. For instance, tires spinning and low vehicle speed may be monitored and identified to determine that vehicle movement was being affected by the weather (as compared to the technology, functionality, system, and/or equipment during normal operation). Vehicle tires may spin with little or no vehicle movement in snow, rain, mud, ice, etc.
  • The data gathered may indicate a current version of artificial intelligence or computer instructions that the autonomous or semi-autonomous system or equipment is utilizing. A collision risk factor may be assigned to each version of computer instructions. The insurance provider may then adjust or update insurance policies, premiums, rates, discounts, and/or other insurance-related items based upon the collision risk factor and/or the artificial intelligence or computer instruction versions presently employed by the vehicle (and/or upgrades there to).
  • C. Outside Data
  • The decision and operating data gathered may be merged with outside data, such as information related to weather, traffic, construction, and/or other factors, and/or collected from sources besides the vehicle. In some embodiments, such data from outside the vehicle may be combined with the control decision data and other operating data discussed above to determine risks associated with the operation of one or more autonomous or semi-autonomous operation features. External data regarding the vehicle environment may be requested or received via the network 130 and associated with the entries in the log or database based on the timestamp. For example, the location, date, and time of a timestamp may be used to determine weather and traffic conditions in which vehicle operation occurred. Additional external data may include road conditions, weather conditions, nearby traffic conditions, type of road, construction conditions, presence of pedestrians, presence of other obstacles, and/or availability of autonomous communications from external sources. For instance, weather may impact certain autonomous or semi-autonomous technology, functionality, system, and/or equipment performance, such as fog, visibility, wind, rain, snow, and/or ice. Certain autonomous or semi-autonomous functionality may have degraded performance: (1) on ice covered roads; (2) during snow or rain, and/or on snow or rain covered roads; (3) during poor visibility conditions, such as foggy weather; (4) in “stop and go” traffic, such as during rush hour traffic, or slow moving traffic through high construction areas or downtown areas; and/or (5) caused by other factors.
  • The system and method may consider the geographical area associated with the user, or the owner or operator of a vehicle. For instance, rain mitigation functionality or technology for vehicles may be pertinent to reducing the amount of accidents and/or the severity of such accidents in areas of high rain fall, such as the Pacific Northwest or Florida. On the other hand, such functionality may have less of a beneficial impact on accidents or potential accidents in desert locations, such as Nevada or New Mexico.
  • Construction-related data may also be collected and analyzed. Construction-related accident avoidance and/or mitigation technology, functionality, systems, or associated equipment may be more pertinent in large urban areas involving significant and lengthy construction or road connector projects that may include frequently changing travel patterns with little notice to drivers.
  • D. Autonomous Vehicle Telematics
  • The data gathered may relate to autonomous vehicle telematics variables. From which, usage-based insurance policies, premiums, rates, discounts, rewards, and/or other insurance-related items may be estimated, as discussed elsewhere herein.
  • For instance, if sensor data indicates that automatic braking is only used by the driver 50% of the time, an updated or adjusted insurance policy, premium, rate, and/or discount may be estimated for the driver, such as by a remote processor or server associated with the insurance provider. A message may be wirelessly communicated to the vehicle or mobile device associated with the driver that indicates that they may save a given amount of money on their auto insurance if they increase usage of the automatic braking technology or functionality to a certain percentage of time, such as up to 90% of vehicle driving time for example. Usage of other technologies and functionalities (including the technologies and functionalities discussed elsewhere herein) may be monitored, and recommended usages thereof (and associated insurance savings) may be provided to the insured or driver for their review and/or approval.
  • Other manners of saving money on existing auto insurance coverage may be provided to the driver via wireless communication. For instance, a percentage of time that the vehicle is in a (1) “manual” mode or operation; (2) semi-automated, semi-automatic, or “semi-autonomous” mode or operation; and/or (3) fully automated, fully automatic, or fully “autonomous” mode or operation may be determined from vehicle sensor data that is remotely collected, such as at or by an insurance provider remote processor or server.
  • The insurance provider remote processor or server may determine auto insurance discounts increases or premium reductions based upon proposed changes to the time that the vehicle is operated in each mode, i.e., manual, semi-autonomous, or fully autonomous. For instance, driving in a semi-autonomous, or even autonomous mode, of operation may be the safest for a given technology or functionality and/or under certain driving conditions (e.g., freeway driving in clear weather and moderate traffic). The driver may be offered a reduced insurance premium or rate to increase usage of the semi-autonomous, or even autonomous, technology or functionality, and/or to increase usage of the semi-autonomous, or even autonomous, technology or functionality in certain driving conditions.
  • Additionally or alternatively, the insurance provider may offer a plurality of separate tiers of auto insurance policies, premiums, rates, discounts, etc. For example, the insurance provider may offer three separate tiers. The three separate insurance tiers of premiums, rates, discounts, etc. may be based upon (a) a manual insurance rate; (b) a semi-autonomous insurance rate; and/or (c) a fully autonomous insurance rate. The manual insurance rate may be associated with manual operation of the vehicle; the semi-autonomous insurance rate may be associated with semi-autonomous operation of the vehicle; and/or the fully autonomous insurance rate may be associated with autonomous operation of the vehicle.
  • Also, the data gathered may be used to provide feedback to the customer or insured. For instance, if the vehicle is presently traveling on the highway, a recommendation or offer may be presented to the driver, such as via wireless communication with the vehicle that indicates that if the driver places the vehicle into autonomous or semi-autonomous driving mode, the risk of collision may be reduced and/or the driver may be receive a discount, and/or lower premium on his or her auto insurance.
  • Other manners of potential risk reductions may also be communicated to the driver or owner of the vehicle. For instance, recommendations and/or adjustments to insurance policies, premiums, rates, discounts, rewards, and/or other insurance-related items may be based upon driver characteristics or age, such as beginning or teenage drivers.
  • As an example, auto insurance policies, premiums, rates, discounts, rewards, and/or other insurance-related items may be adjusted, updated, or generated based upon (1) the autonomous or semi-autonomous technology and/or functionality; (2) an amount or percentage of driver usage of that technology and/or functionality; and/or (3) driver characteristics. The driver characteristics that may be taken into consideration include driver age, driver health, and/or past driving or accident history.
  • E. Smart Equipment
  • The data gathered may originate from various smart parts and/or pieces of smart equipment mounted on a vehicle, including parts configured for wired or wireless communication. For instance, a vehicle may be equipped with smart brakes; smart tail, head, or turn lights; smart tires; etc. Each piece of smart equipment may have a wired or wireless transmitter. Each piece of smart equipment may be configured to monitor its operation, and/or indicate or communicate a warning to the driver when it is not operating properly.
  • As an example, when a rear brake light is out, such as from faulty repair or from normal burn out, that fact may be detected by smart vehicle functionality and the driver may be promptly notified. As a result, the driver may be able to repair the faulty brake light before an accident caused by the faulty brake light occurs. In another embodiment, the data gathered may also indicate window wipers are not operating properly, and need to be replaced. The insurance provider may adjust or update insurance policies, premiums, rates, discounts, and/or other insurance-related items based upon the smart equipment warning functionality that may alert drivers of vehicle equipment or vehicle safety equipment (lights, brakes, etc.) that need to be replaced or repaired, and thus may reduce collision risk.
  • In addition to addressing liability for collision risk, the technology may also reduce risk of theft. For instance, stolen vehicles may be tracked via on-board GPS units and wireless transmitters. Also, the breaking and entering, and/or hot wiring, of vehicles may be more difficult through the use of anti-hacking measures for smart vehicles or vehicles with electrical or electronic control systems. The insurance provider may adjust insurance premiums, rates, and/or other insurance-related items based upon the reduced risk of theft.
  • Assignment of Fault
  • The present embodiments may relate to the assignment of fault. The assignment of fault may be based upon sensor data and/or other data gathered or collected from, or by, the vehicle. The assignment of fault may impact the future rating for one or more drivers, and/or one or more vehicles equipped with one or more autonomous or semi-autonomous technologies, functionalities, systems, and/or pieces of equipment.
  • The assignment of fault determination from sensor and/or vehicle data may relate to, and/or involve, determining who was in control of, or driving, the vehicle at the time of the accident (such as either the human driver or the vehicle itself), and/or determining who was at fault or liable for the collision or accident—the human driver or the vehicle. For instance, did the vehicle give the driver enough time (e.g., half a second) to take manual control of the vehicle before the time of impact, or was the driver not attentive enough before an accident.
  • The assignment of fault may include a determination of who pays the claim associated with a vehicle accident and/or determine future ratings for certain types of technology or functionality, and/or certain insurance policy holders. Fault for a vehicle collision or accident may be partially or fully assigned to one or more drivers, and/or one or more vehicles equipped with one or more autonomous or semi-autonomous technologies, functionalities, systems, and/or pieces of equipment.
  • In one embodiment, insurance coverage may provide for immediate coverage/payment to an insured in the case of an accident. After which, based upon data collected from the smart vehicle or sensors, blame or fault may be assigned for the accident, such as to either the driver or the autonomous or semi-autonomous technology or functionality, and/or to another driver, or autonomous or semi-autonomous vehicle involved in the accident.
  • Alluded to above, the data gathered may help determine who was in control of the vehicle before, during, and/or after a vehicle collision or accident. For instance, a human driver, or an autonomous or semi-autonomous vehicle (and/or associated technology, functionality, system, and/or equipment) may have been in control of the vehicle at the time of accident. The data may be used to identify whether there was enough time for a driver to takeover manually. For instance, once a hazardous condition is identified (e.g., vehicles slowing down abruptly or heavy congestion ahead, or vehicle accident ahead), did a semi-autonomous technology function correctly, and/or did the human driver have the time to take manual control of the vehicle and avoid a collision or accident.
  • For a fully autonomous vehicle, technology, or functionality whether or not the collision or accident could have been avoided may be determined. For example, a performance of the artificial intelligence or computer instructions associated with the autonomous vehicle, technology, or functionality may be evaluated. In accidents or collisions involving one, two, or more autonomous vehicles, evaluating the performance of the autonomous technology or functionality may determine fault—such as which autonomous vehicle was at fault (for an accident involving two autonomous vehicles) or whether an autonomous vehicle or a human driver was at fault (for an accident involving two vehicles, one driven by a human driver and one driven by autonomous vehicle technology, functionality, or systems that may include associated artificial intelligence and/or processors).
  • Insurance Adjustment Recommendations
  • Autonomous or semi-autonomous technology, functionality, and/or system usage data may be used to identify and present a driver one or more potential premium or rate reductions with increased usage of the technology, functionality, and/or system. A number of “what if” insurance-related scenarios may be calculated and then presented to a driver and/or insured for their review, approval, and/or modification. The different scenarios may be presented to a driver on their mobile device or a smart vehicle display screen, or other in dash display.
  • Autonomous or semi-autonomous vehicle technology or functionality may relate to vehicle parking. The technology or functionality may determine an available parking spot in an urban area or large city. The smart vehicle may make recommendations to the driver regarding the best available parking spot remotely identified. For instance, the best available parking spot may be determined based upon the cost of the parking; safety of the parking spot, lot, or garage; the risk of theft or other liability associated with the parking spot, lot, or garage; and/or other factors.
  • The recommendation may be generated by an insurance provider remote processor or server. The recommendation with respect to best available parking spot may include information regarding an adjustment to the driver's present insurance policy, premium, rate, and/or discount based upon the driver accepting the recommendation and parking the vehicle in the best available parking spot. A discount and/or lower premium may be offered to the driver to encourage safer parking habits that may reduce the risk of vehicle damage or theft.
  • Alternatively, based upon an actual parking spot, additional insurance coverage may be offered to the driver. For instance, if the vehicle is going to be parked on a busy street and overnight, it may have a higher risk of damage or theft. A remote processor or server associated with the insurance provider may estimate and/or offer an appropriate increase in auto insurance coverage to the insured or driver, such as via wireless communication with a smart vehicle controller or a mobile device of the insured or driver.
  • Exemplary Feedback Method
  • Beyond determining risk categories or premiums for vehicle insurance policies covering autonomous (and/or semi-autonomous) vehicles, in some embodiments the system 100 may operate to monitor use of autonomous (and/or semi-autonomous) operation features and present feedback to vehicle operators. This may occur in real time as operating conditions change or may occur on a periodic basis in response to vehicle use and environmental conditions. The use of autonomous operation features may be assessed to determine whether changes to the number, type, configuration, or settings of the autonomous operation features used may reduce the risk associated with vehicle operation under various conditions. Presenting or otherwise providing the information to the vehicle operator may improve the effective use of the autonomous operation features and/or reduce the risks associated with vehicle operation. Upon receiving a suggestion regarding autonomous operation feature use, the vehicle operator may be able to maximize the effectiveness of the autonomous operation feature, maximize vehicle insurance coverage, and/or minimize vehicle insurance expense.
  • FIG. 11 illustrates an exemplary autonomous (and/or semi-autonomous) operation feature monitoring and feedback method 1100. The method 1100 may be performed by the controller 204 or the server 140 at any time while the vehicle 108 is in operation. In some embodiments, the method 1100 may be implemented only when the vehicle 108 is stationary, when the autonomous (and/or semi-autonomous) operation features are controlling the vehicle 108, when the controller 204 or server 140 determines that the conditions meet certain criteria (e.g., when the vehicle is more than a predetermined distance from environmental obstacles on a restricted access highway, etc.), and/or when the vehicle 108 is first started, such that the method 1100 does not distract the vehicle operator. During implementation of the method 1100, the controller 204 may determine actual use levels of the autonomous operation features at block 1102. This may include current use of the features and/or past use of the features, either generally or under similar conditions. In some embodiments, the determination of use levels may include a determination of the use of versions, configurations, or settings related to the autonomous operation features.
  • At block 1104, the controller 204 may receive sensor data from the sensors 120, as discussed above. The received sensor data may include information regarding the vehicle 108, the vehicle's environment (e.g., traffic conditions, weather conditions, etc.), and/or the vehicle operator. The sensor data may include information regarding the physical or mental state of the vehicle operator using sensors 120 disposed within the vehicle 108 or communicatively connected thereto (e.g., disposed within or communicatively connected to a mobile device 110, such as a smart phone, and/or a wearable computing device, such as a smart watch or smart glasses). This sensor data may include data from interior cameras, microphones, accelerometers, and/or physiological sensors (e.g., thermometer, microphone, thermal image capture device, electroencephalograph, galvanic skin response sensor, heart rate sensors, respiratory rate sensor, other biometric sensors, etc.). In some embodiments, the received sensor data may exclude sensor data regarding the vehicle operator or the physical or mental state of the vehicle operator.
  • At block 1106, the controller 204 or the server 140 may receive communication data from external sources. The communication data may include direct communication data from other autonomous vehicles, communicating infrastructure, and/or other smart devices (e.g., mobile devices carried or worn by pedestrians, passengers in other vehicles, etc.). The communication data may also include indirect communication data received by the controller 204 or the server 140 via the network 130 (e.g., information regarding traffic, construction, accidents, weather, local time, local events, local traffic patterns, local accident statistics, general accident statistics, etc.). The indirect information may be obtained from database 146 or from other networked or third-party databases. For example, indirect communication data may be obtained regarding the risk level of autonomous operation features relative to manual vehicle operation on major highways during typical commuting times in urban areas in light rain, which may be combined with information from a weather service indicating light rain and information from a map service indicating the vehicle 108 is on a major highway (using GPS data from the sensors 120). As a further example, traffic databases could be accessed to receive information regarding accidents and/or construction further ahead along a route.
  • At block 1108, the server 140 or the controller 204 may determine an optimal use level for the autonomous operation features available within the vehicle 108 and/or a suggestion regarding the optimal autonomous operation feature use level under the conditions. The optimal use level or suggestion may include the types and versions of autonomous operation features to use, configurations of the features, and/or settings relating to the features. The server 140 or the controller 204 may determine one or more optimal use levels for the autonomous operation features based upon the sensor data and communication data received in blocks 1104 and 1106 using any known or later-developed optimization techniques. In some embodiments, the risk levels associated with each combination of use levels for autonomous operation features may be determined and stored in one or more databases, such that the server 140 or controller 204 may access and compare the appropriate database entries to determine the optimal use levels. In further embodiments, one or more optimal use levels may be determined and stored in one or more databases, such that the server 140 or controller 204 may determine the optimal use level by accessing the database entry corresponding to the sensor data and communication data. Alternatively, the server 140 or controller 204 may determine optimal use levels by determining risk levels for a variety of combinations of configurations and settings associated with autonomous operation features based on the received sensor data and communication data. In such embodiments, the combination or combinations determined to have the lowest risk may be determined to be the optimal feature use levels.
  • The determination of optimal feature use may be based upon the received sensor data and/or the communication data. In some embodiments, the received sensor data and/or communication data may include information regarding the physical, mental, and/or emotional state of the vehicle operator, as noted above. In various embodiments, the determination of the optimal feature use level may either include or exclude information regarding the state of the vehicle operator from the determination. For example, the determination may be based in part upon the previous driving history of a vehicle operator, which may indicate that the vehicle operator has an increased risk of an accident in low light environments. In the example, the determination may compare the expected performance of the various autonomous operation features against the expected performance of the vehicle operator, which may cause the server 140 or controller 204 to determine an optimal feature use level that includes more autonomous operation feature use than would otherwise be determined to be optimal. As a related example, the server 140 or the controller 204 may not determine the optimal use level based upon the previous driving history of the vehicle operator from the previous example, which may result in a determination of an optimal feature use level that includes less use of autonomous operation features than in the preceding example.
  • The determined optimal use level may be used to further determine an autonomous (and/or semi-autonomous) operation feature use suggestion. The use suggestion may include one or more settings relating to autonomous operation features, enabling or disabling particular autonomous operation features, using specific versions of autonomous operation features, resuming manual operation of the vehicle, temporarily ceasing autonomous and/or manual operation of the vehicle, and/or similar changes to the use and configuration of the autonomous operation features in operating the vehicle 108. It should be noted that the determined use suggestion may include changes to the use of autonomous operation features, use of additional autonomous operation features, and/or use of fewer autonomous operation features.
  • At block 1110, the suggested optimal use levels of autonomous (and/or semi-autonomous) operation features determined at block 1108 is compared against the actual autonomous operation feature use levels determined at block 1102. When the suggested and optimal feature use levels are determined to be different, the server 140 or the controller 204 causes a suggestion of autonomous operation feature use to be presented to the vehicle operator at block 1112. In some embodiments, the suggestion may not be presented when the difference between the optimal use level and the actual use level is below a predetermined threshold. For example, the server 140 may determine not to present the suggested autonomous operation use to the vehicle operator where the difference would only result in a risk reduction equivalent to a monetary value below five cents.
  • The suggestion presented at block 1112 may be presented using a display within the vehicle 108, a mobile device 110, or other means, including visual and/or audible notifications. The suggestion may include a recommendation that the vehicle operator enable or use one or more additional autonomous (and/or semi-autonomous) operation feature, that the vehicle operator change the settings or configuration for one or more autonomous (and/or semi-autonomous) operation features, that the vehicle operator disable or discontinue use of one or more autonomous (and/or semi-autonomous) operation features, and/or related changes that may be made to the use of the autonomous (and/or semi-autonomous) operation features. The suggestion may further include one or more reasons for making a change to the autonomous operation feature use, such as an indication of a reduction in risk, a percentage reduction in the probability of a collision, an increase in a probability of completing the trip without incident, a reduction in a premium or other policy charge, a reduction in a rate, an increase in a coverage amount, an increase in a coverage type, a reduction in a deductible, and/or related information to induce the vehicle operator to change the autonomous operation feature use. For example, a suggestion presented to the vehicle operator may indicate that updating to a newer software version of an autonomous operation feature would result in a decrease of a certain amount in a vehicle insurance premium. In some embodiments, the vehicle operator may make a selection upon presentation of the suggestion, which selection may cause the use levels of one or more of the autonomous operation features to be adjusted (e.g., to match the one or more optimal use levels). In other embodiments, the vehicle operator may otherwise adjust or control the use levels, as discussed above. A change or adjustment to the use, configuration, or settings of the autonomous operation features may further cause a change or adjustment to costs or coverage associated with a vehicle insurance policy, as discussed above.
  • After a suggestion has been presented at block 1112 or when the suggested optimal feature use is determined to be insufficiently different from the actual feature use at block 1110, the server 140 or the controller 204 determine whether vehicle operation is ongoing at block 1114. When operation is ongoing, the method 1100 may repeat the steps of blocks 1102-1112. In some embodiments, the method 1100 may repeat only when a predetermine period of time (e.g., 5 minutes, 15 minutes) has passed, when vehicle operating conditions have sufficiently changed (e.g., upon exiting a highway, entering fog, sunset, etc.), and/or when a sufficient change in the recommendation has occurred (e.g., risk level, monetary incentive, feature use level recommendation, etc.). When the operation of the vehicle 108 is complete, the method 1100 may terminate. In some embodiments, however, the method 1100 may be implemented either before or after vehicle operation, in which case the actual autonomous (and/or semi-autonomous) operation feature use determined in block 1102 may be based upon the settings of the autonomous operation features that had been previously used, the settings that would be applied if the vehicle were to be used at that time, or the default settings.
  • Exemplary Warning Method
  • In addition to monitoring use of autonomous operation features to present feedback regarding autonomous (and/or semi-autonomous) operation feature use to vehicle operators, some embodiments may determine elevated risk levels and present warnings to the vehicle operator. In some embodiments, this may include warnings regarding situations where no changes to the optimal autonomous operation feature use level would be suggested, but where an increased risk nonetheless exists. For example, communication data regarding recent snowfall may be combined with sensor data indicating a high frequency of slipping wheels to determine a high risk of an accident exists at the current speed on a snow-covered road. The vehicle operator might then respond by reducing the speed of the vehicle, resuming manual control of the vehicle, and/or selecting an alternate route using major thoroughfares that are clear of snow. Such responses may further cause an adjustment in a cost or coverage level associated with a vehicle insurance policy.
  • FIG. 12 illustrates an exemplary autonomous (and/or semi-autonomous) operation feature monitoring and alert method 1200. The method 1200 may be performed by the controller 204 or the server 140 at any time while the vehicle 108 is in operation. During implementation of the method 1200, the controller 204 or server 140 may determine the use of the autonomous operation features at block 1202. This may include current use of versions, configurations, or settings related to the autonomous operation features. As discussed above, the controller 204 or server 140 may further receive sensor data and communication data, respectively, at blocks 1204 and 1206. The sensor data may be received from sensors 120 disposed within the vehicle 108, and the communication data may include information regarding the vehicle environment (including information regarding the rate of incidents in similar conditions or locations based on historical data). This information may be used at block 1208 to determine the risk associated with operation of the vehicle under the conditions. As above, the sensor data, communication data, and the determination of risk may either include or exclude information regarding one or more vehicle operators (e.g., the physical, mental, and/or emotional state of the vehicle operator).
  • At block 1208, the server 140 or the controller 204 may determine a risk level associated with the operation of the vehicle under the current conditions. This may include a determination of the risk associated with the autonomous (and/or semi-autonomous) vehicle operation features then in use, or it may include a determination of the risk associated with various configurations or settings of autonomous operation features as discussed above with respect to method 1100. In some embodiments, the determination may not include information regarding one or more vehicle operators. The server 140 or controller 204 may determine one total risk level or a plurality of risk levels associated with vehicle operation at block 1208. For example, separate risk levels may be determined for different types of potential incidents (e.g., collisions with other vehicles, loss of control or traction, collisions with pedestrians, collisions with stationary obstructions, etc.).
  • At block 1210, the server 140 or the controller 204 may compare the determined risk level against a warning threshold risk level. In some embodiments, the difference between the determined risk level and a risk level associated with an optimal autonomous (and/or semi-autonomous) operation feature use level (as discussed above with respect to method 1100) may be compared against the warning threshold, and the warning threshold may be set at a level such that a warning is triggered only when the additional risk from suboptimal autonomous (and/or semi-autonomous) operation feature use, configuration, and/or settings exceeds a certain level. In further embodiments, the risk level may be compared against a plurality of predetermined warning thresholds, and the warning presented to the vehicle operator may be determined based upon the highest warning threshold exceeded by the risk level.
  • When the risk level is determined to exceed the warning threshold at block 1210, the controller 204 or server 140 may cause a warning to be presented to the vehicle operator at block 1212. The warning presented at block 1212 may be presented using a display within the vehicle 108, a mobile device 110, or other means, including visual, audible, and/or haptic notifications. The warning may specify one or more causes of the elevated risk (e.g., weather, speed, hardware malfunctions, etc.). Alternatively, the warning may simply alter the vehicle operator to an elevated risk level. In some embodiments, the vehicle operator may make a selection upon presentation of the alert, which selection may cause the use, configuration, or settings of one or more of the autonomous (and/or semi-autonomous) operation features to be adjusted (e.g., the vehicle operator may resume full control of operation, the vehicle operator may cede control of operation to the autonomous (and/or semi-autonomous) operation features, etc.). In other embodiments, the vehicle operator may otherwise adjust or control the use levels, as discussed above. A change or adjustment to the use, configuration, or settings of the autonomous operation features may further cause a change or adjustment to costs or coverage associated with a vehicle insurance policy, as discussed above.
  • After the warning has been presented at block 1212 or when the risk level is determined to be below the risk threshold at block 1210, the server 140 or the controller 204 determine whether vehicle operation is ongoing at block 1214. When operation is ongoing, the method 1200 may repeat the steps of blocks 1202-1212. When the operation of the vehicle 108 is complete, the method 1200 may terminate.
  • Exemplary Fault Determination Method
  • In some embodiments, the system 100 may be used to determine or allocate fault upon the occurrence of an accident or other collision involving the vehicle 108. Information regarding the operation of the vehicle 108 may be recorded and stored during operation, which may then be used to determine the cause of a collision or accident automatically upon receiving an indication of the occurrence of such. Fault may be allocated to either the vehicle operator, one or more autonomous operation features, or a third party (e.g., another motorist or autonomous vehicle). Such allocation of fault may be further used to adjust one or more of an insurance policy premium, a risk level, a rate category, a penalty, or a discount relating to a vehicle insurance policy. In some embodiments, the allocation of fault may also be used to determine whether to cancel an insurance policy, adjust a deductible, adjust a policy limit, and/or determine a payment associated with the collision or accident. Where an autonomous operation feature is determined to be wholly or partially responsible for the accident, the risk levels or risk profile associated with that autonomous operation feature may be revised, such that the risk levels or risk profile of other autonomous vehicles using the feature may also be adjusted.
  • FIG. 13 illustrates an exemplary fault determination method 1300 for determining fault following an accident based upon sensor data and communication data. Upon receiving an indication of an accident at block 1302, the method 1300 may receive sensor data and communication data at block 1304 and may further receive information regarding the operation of one or more autonomous (and/or semi-autonomous) operation features at block 1306. In some embodiments, this information may be used to make a preliminary determination of whether a third party is at fault at block 1308, in which case there may be no fault allocated to the vehicle operator and/or autonomous (and/or semi-autonomous) operating features. If a third party was not at fault or if the vehicle 108 had the last chance to avoid the accident, the method 1300 may then determine and allocate fault between the vehicle operator and one or more autonomous (and/or semi-autonomous) operation features in blocks 1314-1324. The determination of fault may further be used to determine and/or adjust a coverage level at block 1326, such as a deductible level or a policy status.
  • The determination process of method 1300 may depend upon whether the vehicle 108 is operated in a fully autonomous, partially autonomous, or manual operation mode at the time of the accident. In some embodiments, the server 140 may determine and/or allocate fault without human involvement. In other embodiments, the server 140 may present relevant information and/or a determination of fault to a reviewer (e.g., a claims adjuster or other specialist) for verification or further analysis. In such embodiments, the presented information may include summaries or detailed reports of sensor data and/or communication data, including still images or video recordings from the sensors 120 within the vehicle 108 or other sensors at the location of the accident (e.g., sensors disposed within other vehicles involved in or near the accident site, sensors disposed within infrastructure elements, etc.). The method 1300 may be implemented by the mobile device 110, the on-board computer 114, the server 140, and/or some combination of these components.
  • At block 1302, the server 140 may receive an indication of an accident involving the vehicle 108. The server 140 or controller 204 may generate this indication automatically based on sensor data, or it may be initiated manually by a vehicle operator or another person following the accident. However the indication is received, it may cause the method 1300 to proceed to the one or more determinations of fault.
  • At block 1304, the server 140 may receive sensor data from the one or more sensors 120 within the vehicle 108 and/or communication data from the communication component 122 and/or the communication unit 220. In addition, the server 140 may receive additional information from external sources, including sensor data from other vehicles or infrastructure, communication information from other vehicles or infrastructure, and/or communication information from third-party sources. For example, additional information may be obtained from other autonomous vehicles involved in the accident or near the accident. As discussed above, the server 140 may additionally receive control decision data regarding the control decisions generated by one or more of the autonomous operation features of the vehicle 108. In some embodiments, the sensor and/or communication data may be stored in the database 146 or in the program memory 160 or 208, and/or in the RAM 164 or 212 during ordinary operation of the vehicle 108, from which the data may be retrieved or accessed by the server 140. Additionally, or alternatively, the sensor and/or communication data may be stored in another memory or database communicatively connected to the network 130. In some embodiments, a back-up of the sensor and/or communication data may be stored within a memory (not shown) that may be designed to withstand the forces and temperatures frequently associated with a vehicular collision.
  • At block 1306, the server 140 may further receive information regarding the operation of the autonomous (and/or semi-autonomous) operation features in the vehicle 108. This information may include information regarding use, configuration, and settings of the features concurrent with the accident. In some embodiments, the information may further include information regarding control signals or outputs from the autonomous operation features to control the vehicle 108. This may be useful, for example, in determining whether the autonomous operation feature failed to take appropriate control actions or whether the control signals were not implemented or were ineffective in controlling the vehicle 108 (e.g., such as may occur when on ice or when a defect prevents an electromechanical control from properly functioning). In some embodiments, autonomous operation feature data may be available for additional vehicles involved in the accident, which may be accessed or obtained by the server 140. As above, the autonomous operation feature data may be recorded during ordinary operation of the vehicle 108 and accessed or obtained by the server 140 upon receipt of the indication of the accident.
  • At block 1308, the server 140 may determine whether a third party is at fault for the accident based upon the sensor data, communication data, and/or autonomous (and/or semi-autonomous) operation feature data received in blocks 1304 and 1306. Determining fault may generally include determining one or more of the following: a point of impact on the vehicle 108, a point of impact on one or more additional vehicles, a velocity of the vehicle 108, a velocity of one or more additional vehicles, a movement of the vehicle 108, a movement of one or more additional vehicles, a location of one or more obstructions, a movement of one or more obstructions, a location of one or more pedestrians, a movement of one or more pedestrians, a measure of road surface integrity, a measure of road surface friction, a location of one or more traffic signs or signals (e.g., yield signs, stop signs, traffic lights, etc.), an indication of the state of one or more traffic signs or signals, a control signal generated by one or more autonomous operation features of the vehicle 108, and/or a control signal generated by one or more autonomous operation features of one or more additional vehicles. Based upon the above-mentioned factors, the server 140 may determine whether the vehicle 108 (including the vehicle operator and/or the autonomous operation features) caused the accident or whether a third party (including other autonomous vehicles, other vehicle operators, pedestrians, etc.) caused the accident.
  • For purposes of determining fault at block 1310, in some embodiments the server 140 may include unavoidable accidents as being the fault of a third party (e.g., a bridge collapse, an animal suddenly darting into the path of a vehicle, etc.). Additionally, or alternatively, physical defects in the autonomous vehicle 108 or the physical components of the autonomous operation features (e.g., the sensors 120, the on-board computer 114, or connections within the vehicle 108) may be determined by the server 140 as being the fault of a third party (e.g., the vehicle maker, the original equipment manufacturer, or the installer).
  • When the accident is determined at block 1310 to have been caused by a third party, the server 140 may then determine whether the vehicle 108 or the vehicle operator had a chance to avoid the accident that was not taken at block 1312. For example, the vehicle operator may have been able to avoid a collision by braking or swerving but for inattentiveness at the time of the accident. Where no such chance for the vehicle operator or the autonomous operation features to avoid the accident is determined to have existed at block 1312, the fault determination method 1300 may terminate. Where such a chance to avoid the accident is determined to have existed at block 1312, the method 1300 may continue to allocate a portion of the fault between the vehicle operator and the autonomous operation features.
  • At block 1314, the server 140 may determine the operating control status of the vehicle 108 at the time of the accident based upon the received autonomous (and/or semi-autonomous) operation feature data regarding the use, configuration, and settings of the features. The vehicle 108 may be determined to have been either manually, fully autonomously, or partially autonomously operated at the time of the accident. Based upon the determination, the allocation of fault will be determined differently. Of course, any allocation of fault to a third party above at block 1310 may decrease the total fault to be allocated between the vehicle operator and the one or more autonomous operation features.
  • Where it is determined at block 1314 that the vehicle 108 was operating entirely manually without any autonomous operation features at the time of the accident, the fault may be allocated entirely to the vehicle operator. In such case, the server 140 may adjust (or cause to be adjusted) the risk or rate profile associated with the vehicle operator at block 1322 in a manner similar to the adjustment that is typically made when a vehicle operator of a non-autonomous vehicle is determined to be at fault for an accident.
  • Where it is determined at block 1314 that the vehicle 108 was operating in a fully autonomous mode at the time of the accident, the fault will usually be assigned entirely to one or more autonomous operation features. There are some situations, however, where the autonomous operation feature may recognize a situation where autonomous operation is no longer feasible due to conditions in the vehicle's environment (e.g., fog, manual traffic direction, etc.). When it is determined that the vehicle 108 was operating as a fully autonomous vehicle at block 1314, therefore, the server 140 may determine whether the one or more autonomous operation features attempted to return control of the vehicle to the vehicle operator prior to the accident at block 1318. Because such attempts may require the vehicle operator to be alert and capable of receiving control from the autonomous operation features, an adequate period of time for transition may be required. Thus, when it is determined at block 1320 that the autonomous operation features did not attempt to return control of the vehicle 108 to the vehicle operator or failed to provide sufficient time to transfer control, the server 140 may allocate fault for the accident to the one or more autonomous operation features and adjust the risk levels and/or risk profiles associated with the one or more autonomous operation features at block 1324. When it is instead determined that the autonomous operation features attempted to return control of the vehicle 108 to the vehicle operation with adequate time for transferring control at block 1320, the server 140 may allocate fault to the vehicle operator, and the vehicle operator's risk or rate profile may be adjusted at block 1322. In some embodiments, the server 140 may allocate some portion of the fault to each of the vehicle operator and the autonomous operation features where an attempt to return control of the vehicle 108 to the vehicle operator was made, notwithstanding vehicle operator inattention.
  • Where it is determined at block 1314 that the vehicle 108 was operating in a partially autonomous mode at the time of the accident, the server 140 determines an allocation of fault between the vehicle operator and one or more autonomous operation features at block 1316. This determination may include determining which autonomous operation features were in use at the time of the accident, the settings of those autonomous operation features, and whether the vehicle operator overrode the operation of the autonomous operation features. For example, the server 140 may determine that an autonomous operation feature such as adaptive cruise control without lane centering to be fully or primarily responsible for an accident caused by the vehicle 108 striking another vehicle directly ahead in the same lane. In contrast, the server 140 may determine the vehicle operator to be fully or primarily at fault when the same adaptive cruise control without lane centering was engaged when the vehicle 108 struck another vehicle in an adjacent lane. Upon determining the allocation of fault at block 1316, the server 140 may adjust the vehicle operator and/or autonomous operation feature risk levels accordingly in blocks 1322 and/or 1324, respectively. In some embodiments, the use of autonomous operation features may be considered in reducing the adjustment to the vehicle operator risk or rate profile, thereby mediating the impact of the accident on the rates or premiums associated with vehicle insurance for the vehicle operator.
  • At block 1326 the method 1300 may further utilize the fault determinations for underwriting and/or claim administration, in addition to or as an alternative to adjusting one or more risk levels associated with the vehicle operator or the autonomous operation features. The server 140 may further determine and/or adjust one or more coverage levels associated with an insurance policy covering the vehicle 108 based upon the allocation of fault between the vehicle operator and the autonomous operation features. For example, coverage levels may increase or decrease as the portion of the fault allocated to the autonomous operation feature increases or decreases. In some embodiments, the coverage level may be associated with the accident, such as a deductible, an estimate of a cost to repair or replace the vehicle, an estimate of a cost to repair or replace other property, and/or other payments or adjustments to payments associated with damage or injuries arising from the specific accident. In further embodiments, the coverage level may be associated with a general aspect of the insurance policy, such as a type of coverage, a maximum payment with respect to one or more types of coverage, a maximum payment per person, and/or a maximum total payment per accident. For example, where an autonomous operation feature is determined to be at fault at blocks 1314-1324, a deductible associated with the insurance policy may be reduced or eliminated with respect to the accident. Where both the vehicle operator and an autonomous operation feature are determined to be partially at fault, the coverage level may be determined or adjusted based upon the portion of the fault allocated to each, either directly proportionally or otherwise. For example, the deductible for an accident caused in equal part by the vehicle operator and one or more autonomous operation features may be reduced by some amount (e.g., 25%, 50%, 75%). As a further example, some or all of the maximum coverage limits associated with the insurance policy may be increased (e.g., 25% increase in liability coverage, 50% increase in collision coverage, etc.).
  • In some embodiments, the determination and/or adjustment of the coverage levels may include a determination of ongoing coverage levels or coverage status. The server 140 may determine whether to cancel or decline to renew a policy based upon the allocation of fault and/or received information regarding the accident. For example, when the vehicle operator is determined to be wholly at fault or to be above a fault proportion threshold (e.g., the vehicle operator is responsible for 80% of the fault allocated), the server 140 may determine to cancel the policy in accordance with its terms. Alternatively, the server 140 may determine to require or exclude certain types or levels of coverage, either generally or in a manner dependent upon the settings or configuration of one or more of the autonomous operation features. For example, when the vehicle operator is determined to be above a fault proportion threshold, the server 140 may limit renewal options to exclude collision coverage or limit the maximum policy limit of collision coverage. In some embodiments, coverage may further be limited or cancelled based upon the use of specific autonomous operation features, such as where an autonomous operation feature is determined to be wholly or partially at fault. Determinations regarding coverage levels or coverage status may be further based upon information regarding the vehicle and/or the vehicle operator, such as information regarding previous accidents and/or the vehicle operator's history of use of autonomous operation features (including settings used). Further embodiments may require or exclude coverage (or levels of coverage) based upon future use of autonomous operation features. For example, following an accident caused by the vehicle operator driving at night, the server 140 may determine to cancel coverage for future claims arising from manual operation of the vehicle by the vehicle operator at night. Thus, the coverage may only extend to operation of the vehicle during daylight hours or autonomous operation of the vehicle at night.
  • In some embodiments, the server 140 may cause one or more of the adjustments to the insurance policy to be presented to the vehicle operator or other customer. The customer may be presented with one or more options relating to adjustments to the policy (e.g., options to either agree to use autonomous operation features with certain settings or to forego a type of coverage). Where the vehicle operator declines all presented options, the server 140 may cancel or decline to renew the policy. As with determination of fault, in some embodiments, determinations of adjustments or cancellation may be presented to a reviewer (e.g., a claims adjuster or other specialist) for verification or further analysis prior to becoming effective.
  • Once the server 140 has assigned fault, adjusted the vehicle operator's risk or rate profile and/or one or more of the autonomous (and/or semi-autonomous) operation feature risk levels or profiles, and determined or adjusted one or more coverage levels associated with the vehicle insurance policy, the fault determination method 1300 may terminate. The adjusted risk levels or profiles may be used to adjust a premium, surcharge, penalty, rate, or other cost associated with a vehicle insurance policy for the vehicle 108 and/or the vehicle operator.
  • In some embodiments, the fault determination method 1300 may be implemented after payment has been made on claims relating to the accident. Because the sensor, communication, and autonomous operation feature data may be stored for later use, as discussed above, payment may be made shortly after occurrence of the accident. Determination of fault may then be made or verified at a later date. For example, operating data concerning an accident may be stored for later use following the accident, but payment of claims based upon a vehicle insurance policy covering the vehicle may be made before a determination of fault. Alternatively, or additionally, the fault determination method 1300 may be used to preliminarily determine fault immediately or shortly after the occurrence of an accident, and payment of claims may be made based upon such preliminary determination. Review and assessment of the preliminary determination may be completed at a later time, thereby allowing faster processing of claims.
  • Autonomous Vehicle Insurance Policies
  • The disclosure herein relates to insurance policies for vehicles with autonomous operation features. Accordingly, as used herein, the term “vehicle” may refer to any of a number of motorized transportation devices. A vehicle may be a car, truck, bus, train, boat, plane, motorcycle, snowmobile, other personal transport devices, etc. Also as used herein, an “autonomous operation feature” of a vehicle means a hardware or software component or system operating within the vehicle to control an aspect of vehicle operation without direct input from a vehicle operator once the autonomous operation feature is enabled or engaged. Autonomous operation features may include semi-autonomous operation features configured to control a part of the operation of the vehicle while the vehicle operator control other aspects of the operation of the vehicle. The term “autonomous vehicle” means a vehicle including at least one autonomous operation feature, including semi-autonomous vehicles. A “fully autonomous vehicle” means a vehicle with one or more autonomous operation features capable of operating the vehicle in the absence of or without operating input from a vehicle operator. Operating input from a vehicle operator excludes selection of a destination or selection of settings relating to the one or more autonomous operation features.
  • Additionally, the term “insurance policy” or “vehicle insurance policy,” as used herein, generally refers to a contract between an insurer and an insured. In exchange for payments from the insured, the insurer pays for damages to the insured which are caused by covered perils, acts, or events as specified by the language of the insurance policy. The payments from the insured are generally referred to as “premiums,” and typically are paid by or on behalf of the insured upon purchase of the insurance policy or over time at periodic intervals. Although insurance policy premiums are typically associated with an insurance policy covering a specified period of time, they may likewise be associated with other measures of a duration of an insurance policy, such as a specified distance traveled or a specified number of trips. The amount of the damages payment is generally referred to as a “coverage amount” or a “face amount” of the insurance policy. An insurance policy may remain (or have a status or state of) “in-force” while premium payments are made during the term or length of coverage of the policy as indicated in the policy. An insurance policy may “lapse” (or have a status or state of “lapsed”), for example, when the parameters of the insurance policy have expired, when premium payments are not being paid, when a cash value of a policy falls below an amount specified in the policy, or if the insured or the insurer cancels the policy.
  • The terms “insurer,” “insuring party,” and “insurance provider” are used interchangeably herein to generally refer to a party or entity (e.g., a business or other organizational entity) that provides insurance products, e.g., by offering and issuing insurance policies. Typically, but not necessarily, an insurance provider may be an insurance company. The terms “insured,” “insured party,” “policyholder,” and “customer” are used interchangeably herein to refer to a person, party, or entity (e.g., a business or other organizational entity) that is covered by the insurance policy, e.g., whose insured article or entity is covered by the policy. Typically, a person or customer (or an agent of the person or customer) of an insurance provider fills out an application for an insurance policy. In some cases, the data for an application may be automatically determined or already associated with a potential customer. The application may undergo underwriting to assess the eligibility of the party and/or desired insured article or entity to be covered by the insurance policy, and, in some cases, to determine any specific terms or conditions that are to be associated with the insurance policy, e.g., amount of the premium, riders or exclusions, waivers, and the like. Upon approval by underwriting, acceptance of the applicant to the terms or conditions, and payment of the initial premium, the insurance policy may be in-force, (i.e., the policyholder is enrolled).
  • Although the exemplary embodiments discussed herein relate to automobile insurance policies, it should be appreciated that an insurance provider may offer or provide one or more different types of insurance policies. Other types of insurance policies may include, for example, commercial automobile insurance, inland marine and mobile property insurance, ocean marine insurance, boat insurance, motorcycle insurance, farm vehicle insurance, aircraft or aviation insurance, and other types of insurance products.
  • Analyzing Effectiveness of Technology & Functionality
  • In one aspect, the present embodiments may provide a system and method for estimating the effectiveness of one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment on reducing a likelihood, and/or severity, of a vehicle accident, such as depicted in FIG. 14 (discussed further below). The system and method, for each autonomous or semi-autonomous vehicle technology or functionality that is analyzed, may evaluate or utilize the effect or impact of one or more accident-related factors or elements on the effectiveness of the respective autonomous or semi-autonomous vehicle technology or functionality. The analysis or evaluation may determine the impact of each factor or element on how well an autonomous or semi-autonomous vehicle technology or functionality actually performs under certain conditions (such as driving, vehicle, and/or road conditions; accident or vehicle type; and/or other factors).
  • A. Technologies and Functionalities
  • Noted above, a system and method may analyze and/or evaluate the effectiveness of autonomous or semi-autonomous vehicle technology, functionality, systems, and/or equipment. Individual technologies, functionalities, systems, and/or pieces of equipment may be evaluated that are related to: (1) fully autonomous (or driverless) vehicles; (2) limited driver control; (3) automatic or automated steering, acceleration, and/or braking; (4) blind spot monitoring; (5) collision warning; (6) adaptive cruise control; (7) parking assistance; (8) driver acuity or alertness monitoring; (9) pedestrian detection; (10) software security for smart vehicles; (11) theft prevention; (12) artificial intelligence upgrades or updates; (13) GPS functionality; (14) vehicle-to-vehicle wireless communication; (15) vehicle-to-infrastructure one or two-way wireless communication; and/or other technology and functionality, including that discussed elsewhere herein. Each technology or functionality, and/or the accident avoidance and/or mitigation effectiveness thereof, may be analyzed individually and/or in combination with one or more other technologies or functionalities.
  • B. Factors or Elements Impacting Effectiveness
  • The analysis and/or evaluation of the effectiveness of each technology or functionality may determine an impact of one or more factors or elements that may degrade the performance of each autonomous or semi-autonomous vehicle technology or functionality. For instance, each factor or element may lower or limit the effectiveness of an autonomous or semi-autonomous vehicle technology or functionality with respect to accident avoidance and/or limiting the severity of vehicle accidents. The factors or elements may be analyzed individually and/or in combination with one or more other factors or elements.
  • Mentioned above, accident-related or other factors, elements, and/or conditions may impact the effectiveness of an autonomous or semi-autonomous vehicle technology or functionality. The factors, elements, and/or conditions that may be evaluated may include: (1) point of vehicle impact during a vehicle accident; (2) type of road that an accident occurs on; (3) time of day that an accident occurs at; (4) weather conditions associated with an accident; (5) type of trip during which the accident occurred (short, long, etc.); (6) vehicle style for the vehicle(s) involved in an accident; (7) whether the vehicles involved in the accident were equipped with vehicle-to-vehicle wireless communication functionality; (8) whether the vehicle(s) involved in the accident were equipped with vehicle-to-infrastructure or infrastructure-to-vehicle wireless communication functionality; and/or other factors, elements, and/or conditions associated with, or impacting, individual vehicle accidents.
  • An evaluation of the foregoing factors, elements, and/or conditions with respect to multiple vehicle accidents involving vehicles having one or more autonomous or semi-autonomous vehicle technologies or functionalities may indicate or suggest: (a) an overall effectiveness for each individual autonomous or semi-autonomous vehicle technology or functionality, and/or (b) the impact (whether negative or positive) of each factor or element (type of road, type of vehicle; time of day; weather conditions, type of vehicle crash, i.e., point of impact; etc.) on the effectiveness of each autonomous or semi-autonomous vehicle technology, functionality, and/or associated equipment. After which, insurance premiums, rates, discounts, rewards, points, and/or other insurance-related items for vehicles having one or more autonomous or semi-autonomous vehicle technologies or functionalities may be generated, adjusted, and/or updated.
  • C. Applying Driver Characteristics to Auto Insurance
  • Characteristics and/or driving behaviors of individual drivers or customers may also be used to estimate, generate, and/or adjust insurance premiums, rates, discounts, rewards, and/or other insurance-related items for vehicles having one or more autonomous or semi-autonomous vehicle technologies or functionalities. Driver characteristics and/or driver behavior, as well as driver location or home address, may be compared, or analyzed in conjunction, with the factors or elements that may impact the accident avoidance or mitigation effectiveness of each autonomous or semi-autonomous vehicle technology or functionality.
  • For instance, a driver or insured may mainly drive on the highway, during daylight hours, and/or primarily for short commutes to and from work. The driver or insured's vehicle may have certain autonomous or semi-autonomous vehicle technologies or functionalities that have been established to decrease the likelihood of an accident, the severity of any accident, and/or otherwise increase safety or vehicle performance during highway, daylight, and/or short commute driving. If so, the insurance rate, premium, discount, and/or another insurance-related item for the driver or insured may be adjusted in accordance with the estimated lower risk (of accident, and/or severe accident).
  • As one example, the impact of one factor (point of vehicle impact) on the effectiveness of accident avoidance and/or mitigation for an autonomous or semi-autonomous vehicle technology or functionality may be determined. For instance, the impact of head-on collisions on the accident avoidance and/or mitigation effectiveness of automatic braking and/or automatic steering functionality may be analyzed. Also analyzed may be the effect of point of vehicle impact on the accident avoidance and/or mitigation effectiveness of automatic acceleration functionality. The impact of point of vehicle impact on the accident avoidance and/or mitigation effectiveness of other autonomous or semi-autonomous technologies and/or functionalities, including those discussed elsewhere herein, may additionally or alternatively be evaluated.
  • As another example, the impact of another factor (vehicle size or type) on the effectiveness of accident avoidance and/or mitigation for an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment may be determined. For instance, the impact of the vehicle being a compact car, mid-sized car, truck, SUV (sport utility vehicle), etc. on the accident avoidance and/or mitigation effectiveness for blind spot monitoring functionality and/or driver acuity monitoring functionality may be analyzed. The impact of vehicle size or type on the accident avoidance and/or mitigation effectiveness of other autonomous or semi-autonomous technologies and/or functionalities, including those discussed elsewhere herein, may additionally or alternatively be evaluated.
  • As a further example, the impact of another factor (type of road) on the effectiveness of accident avoidance and/or mitigation for an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment may be determined. For instance, the impact of the type of road (whether a freeway, highway, toll way, rural road or two-lane state or county highway, and/or downtown or city street) on the accident avoidance and/or mitigation effectiveness for adaptive cruise control functionality and/or vehicle-to-vehicle functionality may be analyzed. The impact of type of road on the accident avoidance and/or mitigation effectiveness of other autonomous or semi-autonomous technologies and/or functionalities, including those discussed elsewhere herein, may additionally or alternatively be evaluated.
  • Additionally, the amount of time or percentage of vehicle usage that an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment is used by the driver or vehicle operator may be determined from sensor or smart vehicle data. Technology usage information gathered or collected may be used to generate, update, and/or adjust insurance policies, premiums, rates, discounts, rewards, points, programs, and/or other insurance-related items.
  • D. Exemplary System Overview
  • At a broad level, the methods and systems described herein may be viewed as combining information regarding autonomous (and/or semi-autonomous) vehicle operation technology with information regarding environmental or usage elements to evaluate one or more autonomous (and/or semi-autonomous) operation features, determine one or more risk factors for the autonomous (and/or semi-autonomous) operation features, and determine vehicle insurance premiums based upon the risk factors. In some embodiments, the autonomous operation features may include an autonomous driving software package or artificial intelligence for operating an automobile. Evaluation of the autonomous operation features may include evaluating both software and hardware associated with the features in a test environment, as well as evaluating actual loss experience associated with vehicles using the features in ordinary operation (i.e., operation not in a test environment). The risk factors may be associated with the relative ability of the autonomous operation features to make control decisions that avoid accidents and other collisions. The risk factors may be included in determining insurance policy premiums, which may in some embodiments include other factors relevant to the determination of the total risk associated with one or more types of insurance coverage for an autonomous vehicle.
  • FIG. 14 illustrates a high-level flow diagram of an exemplary autonomous (and/or semi-autonomous) automobile insurance pricing system. Information regarding one or more autonomous operation feature technologies is collected, accessed, or otherwise received at block 1402. Such information may relate to one or more of the following technologies: a fully autonomous (driverless) vehicle operating technology, a limited driver control technology, an automatic steering technology, an automatic acceleration and/or braking technology, a blind spot monitoring and/or other information augmenting technology, a collision and/or other warning technology, an adaptive cruise control technology, a parking assist technology, and/or other autonomous operation technologies (including those described elsewhere herein or later developed). The autonomous operation feature technologies of block 1402 may be associated with one or more environmental or usage elements, information regarding which may be collected, accessed, or otherwise received at block 1404. Such information may relate to one or more of the following elements: a point of impact between the autonomous automobile and another object (e.g., another vehicle, an infrastructure component, or another moving or fixed object within the autonomous automobile's environment), a type of road (e.g., a limited access highway, a residential neighborhood street, or a main thoroughfare), a time of day and/or date (e.g., rush hour, weekend, or holiday), a weather condition (e.g., light levels, cloud cover, precipitation, temperature, wind, or ground cover such as ice or snow), a type and/or purpose of vehicle trip (e.g., commuting, interstate travel, or leisure), a vehicle style and/or type, a vehicle-to-vehicle communication, or a vehicle-to-infrastructure communication. The information regarding the elements in block 1404 may be further associated with the information regarding the technology in block 1402. Some technologies may be adapted to utilize information regarding some elements, and some elements may be more relevant to some technologies than to others.
  • The information regarding the technologies and elements may then be used in evaluating the performance of the autonomous (and/or semi-autonomous) operation features. The performance or sophistication of the autonomous operating features (e.g., autonomous driving software or artificial intelligence) may be determined within a test environment at block 1406, as described above. The evaluation may include a variety of combinations of technologies and elements, and one or more risk levels or risk profiles may be determined as part of or based upon the evaluation. In some embodiments, the evaluation may include testing the autonomous operation features on a test track or other test facility by installing the features within a test automobile. The test performance may then be supplemented or compared with actual loss experience information relating to the autonomous operating features in actual driving situations recorded at block 1408. The recorded actual loss experience from block 1408 and/or the evaluated test performance from block 1406 may be used to determine a relative or total risk factor for the autonomous operation features based upon the observed or expected ability of the autonomous operation features to make driving decisions for the autonomous automobile and avoid crashes, collisions, or other losses at block 1410. Based upon the risk factor determined at block 1410, one or more premiums or components of premiums for an automobile insurance policy may be determined at block 1412, as discussed above. These premiums make take into account the risks associated with autonomous operation features or combinations of features, as well as expected environmental or usage conditions, factors, or levels. The premiums determined at block 1412 may then be presented to a customer or potential customer for review, selection, or acceptance and purchase.
  • Exemplary Methods of Evaluating Impact on Effectiveness
  • In one aspect, a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided. The method may include: (a) evaluating, via a processor, a vehicle accident avoidance and/or mitigation effectiveness of, and/or associated with, an autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment under real-world driving conditions, the real-world driving conditions including one or more conditions that effect or impact the likelihood, and/or severity, of a vehicle accident or collision; (b) updating, adjusting, and/or generating an auto insurance policy, premium, rate, and/or discount, via the processor, based upon the accident avoidance and/or mitigation effectiveness of the autonomous or semi-autonomous vehicle technology, functionality, system, or equipment for a vehicle equipped with the autonomous or semi-autonomous vehicle feature, technology, system, and/or piece of equipment; (c) presenting (all or a portion of) the updated, adjusted, and/or generated auto insurance policy, premium, rate, and/or discount to an insured, driver, or owner of the vehicle equipped with the autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment for their review, approval, and/or modification on a display screen associated with a computing device; (d) receiving, via the processor, an approval of and/or modification to the auto insurance policy, premium, rate, and/or discount from the insured, driver, or owner of the vehicle equipped with the autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment; and/or (e) updating an auto insurance policy, premium, rate, and/or discount for, and/or then adjusting appropriate amounts to be charged to, the insured, driver, or owner of the vehicle equipped with the autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment based upon the information received from the insured, driver, or owner of the vehicle equipped with the autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment.
  • The step of (a) evaluating, via the processor, an accident avoidance or mitigation effectiveness of, or associated with, an autonomous or semi-autonomous vehicle technology, functionality, system, or piece of equipment under real-world driving conditions may include: (i) analysis of a plurality of vehicle accidents involving one or more vehicles having the autonomous or semi-autonomous vehicle technology, functionality, system, or piece of equipment, and/or (ii) testing vehicles equipped with the autonomous or semi-autonomous vehicle technology, functionality, system, and/or piece of equipment under real-world conditions and gathering data. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In another aspect, a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided. The method may include: (1) updating, adjusting, and/or generating an auto insurance policy, premium, rate, and/or discount, via a processor, based upon and/or taking into consideration: (a) one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment; (b) conditions and/or factors impacting the effectiveness of each autonomous or semi-autonomous vehicle feature, technology, system, and/or piece of equipment with respect to accident avoidance and/or mitigation; (c) driver or insured actual characteristics or driving behavior, and/or geographical location associated with the driver, insured, or vehicle; and/or (d) driver or insured actual usage of the one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment; (2) presenting on a display (such on a display of a computing device associated with the driver or insured, or a sales agent), all or a portion of, the updated, adjusted, and/or generated auto insurance policy, premium, rate, and/or discount for the driver's or insured's review, approval, and/or modification; (3) receiving and/or accepting the approval and/or modification via wireless communication from the computing device associated with the driver or insured at the processor; and/or (4) processing, handling, and/or updating the auto insurance policy accordingly and/or billing the driver or insured appropriately (via the processor) for the updated or new auto insurance coverage.
  • The one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment may be or include an updated or revised version of computer or processing instructions related to the one or more autonomous or semi-autonomous vehicle features, technologies, systems, and/or pieces of equipment. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • Exemplary Methods of Applying Auto Insurance Risk Factors
  • In another aspect, a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided. The method may include: (i) estimating a risk factor associated with auto insurance, via a processor, based upon (1) one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment; and/or (2) one or more accident-related conditions or factors that impact the effectiveness of the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment, individually and/or as a group or collectively; (ii) generating, updating, and/or adjusting an auto insurance policy, premium, rate, and/or discount, via the processor, based upon the (a) risk factor associated with auto insurance estimated, and/or (b) actual driving characteristics and/or behaviors (such as typical driving patterns/paths/routes, geographical location, type of trips usually taken, etc.) of a driver, an insured, or an owner of a vehicle with having the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment; (iii) presenting (all or a portion of) the auto insurance policy, premium, rate, and/or discount generated, updated, and/or adjusted, under the direction and/or control of the processor, on a display of a computing device (such as a mobile device associated with the driver, insured, or vehicle owner or an insurance representative) for their review, approval, and/or modification; (iv) receiving, at the processor, such as via wireless communication from the computing device, the auto insurance policy, premium, rate, and/or discount approved by the driver, insured, or vehicle owner; and/or (v) processing, handling, and/or updating the new auto insurance policy, premium, rate, and/or discount via the processor such that the customer (driver, insured, vehicle owner or operator) is billed appropriately for the amount of auto insurance coverage agreed upon or purchased.
  • An amount or percentage of driving time that the driver or insured uses the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, or pieces of equipment while driving the vehicle may be used to update, adjust, and/or generate the insurance policy, premium, rate, and/or discount. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In another aspect, a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided. The method may include: (1) applying an accident risk factor associated with one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment to an auto insurance policy, premium, rate, discount, reward, etc. for a driver or insured's vehicle having, or equipped with, the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (via a processor), the risk factor being generated from evaluation of one or more driving and/or accident-related conditions impacting an effectiveness of the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment with respect to accident avoidance and/or mitigation; (2) presenting (under the direction and/or control of the processor) on a display of a computing device (such as a mobile device associated with the driver or insured, or a sales agent) (all and/or portions of) the auto insurance policy, premium, rate, discount, reward, etc. to which the accident risk factor was applied for the driver's or insured's review, approval, and/or modification; (3) receiving and/or accepting the approved and/or modified auto insurance policy, premium, rate, discount, reward, etc. at the processor (such as via wireless communication from the computing device); and/or (4) processing, handling, and/or updating (via the processor) the auto insurance policy for the driver's or insured's vehicle having, or equipped with, the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment accordingly based upon the information received to bill the customer (e.g., driver, insured, or vehicle owner/operator) an appropriate amount for the amount of auto insurance coverage purchased and/or agreed to. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • Exemplary Methods of Evaluating Artificial Intelligence
  • In another aspect, a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided. The method may include (1) determining, via a processor, the automobile accident avoidance and/or mitigation related effectiveness associated with, or for, a revision or update of computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that may be stored on a non-transitory computer readable media or medium), the effectiveness determination taking into consideration: (a) actual vehicle accident information for accidents involving vehicles equipped with the revision or update of the computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment; and/or (b) physical testing of vehicles equipped with the revision or update of the computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment. The method may also include (2) updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount for a vehicle equipped with the revision or update of computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • In another aspect, a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided. The method may include: (1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment; and/or (3) updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount for a vehicle equipped with the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment based upon the increase in accident avoidance or mitigation effectiveness determined. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • Additional Exemplary Methods
  • In one aspect, a computer-implemented method of evaluating risk of autonomous or semi-autonomous vehicle technology may be provided. The method may include (1) generating, via one or more processors, a virtual test scenario, the virtual test scenario including one or more accident-related factors or conditions; (2) applying, via the one or more processors, the virtual test scenario to a package of computer instructions that instruct a vehicle processor to perform an autonomous or semi-autonomous functionality; (3) analyzing, via the one or more processors, a performance of the autonomous or semi-autonomous functionality under virtual conditions associated with the virtual test scenario; (4) determining, via the one or more processors, an insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the package of computer instructions that instruct the vehicle processor to perform the autonomous or semi-autonomous functionality, and/or (5) generating, updating, or adjusting, via the one or more processors, a premium, rate, discount, reward, or other insurance item associated with an insurance policy for an autonomous or semi-autonomous vehicle employing the package of computer instructions that instruct the vehicle processor to perform the autonomous or semi-autonomous functionality based upon the insurance-based risk of, or associated with, the package of computer instructions. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
  • For instance, the one or more accident-related factors or conditions of the virtual test scenario may include road, construction, traffic, other vehicle, and/or weather factors or conditions. The virtual test scenario may include a virtual simulation of virtual traffic traveling on a virtual road, and each virtual vehicle traveling on a virtual route at a virtual speed. Determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the package of computer instructions may include determining whether the package of computer instructions made a correct or proper decision given the road, construction, traffic, other vehicle, and/or weather conditions of the virtual test scenario.
  • In another aspect, a computer-implemented method of evaluating risk of autonomous or semi-autonomous vehicle technology may be provided. The method may include (1) collecting or receiving, via one or more processors, a virtual log of actual or real-world driving performance of autonomous or semi-autonomous functionality from a vehicle (or smart vehicle controller); (2) determining, via the one or more processors, an insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance of the autonomous or semi-autonomous functionality, respectively; and/or (3) generating, updating, or adjusting, via the one or more processors, a premium, rate, discount, reward, or other insurance item associated with an insurance policy for an autonomous or semi-autonomous vehicle employing the autonomous or semi-autonomous functionality based upon the insurance-based risk of, or associated with, the autonomous or semi-autonomous functionality. The method may include additional, less, or alternate functionality, including that discussed elsewhere herein.
  • For instance, determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance may include analysis of: (1) decisions that the autonomous or semi-autonomous functionality, or associated computer instructions, made; and/or (2) the environment and/or operating conditions (e.g., road, construction, traffic, and/or weather conditions) under which the decisions were made. Additionally or alternatively, determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance may include analysis of: (1) evasive maneuvers that the autonomous or semi-autonomous functionality, or associated computer instructions, made (or directed the vehicle to perform); and/or (2) the environment and/or operating conditions (e.g., road, construction, traffic, and/or weather conditions) under which the evasive maneuvers were made. Further, determining, via the one or more processors, the insurance-based risk (e.g., a risk of a vehicle accident) of, or associated with, the autonomous or semi-autonomous functionality from analysis of the virtual log of actual or real-world driving performance may include analysis of (1) how the autonomous or semi-autonomous functionality, or associated computer instructions, respond to other drivers on the road maneuvering or changing speed; and/or (2) grading, rating, or otherwise evaluating the responsiveness of the autonomous or semi-autonomous functionality, or associated computer instructions, to other drivers' driving behavior.
  • In another aspect, a computer-implemented method of evaluating risk of autonomous or semi-autonomous vehicle technology and/or adjusting autonomous or semi-autonomous vehicle technology may be provided. The method may include (1) determining, via one or more processors, an optimum setting for an autonomous or semi-autonomous vehicle technology; (2) detecting, via the one or more processors, that an actual setting for an autonomous or semi-autonomous vehicle system of a vehicle is different than the optimum setting for the autonomous or semi-autonomous vehicle technology; (3) generating a recommendation, via the one or more processors, to change the actual setting for the autonomous or semi-autonomous vehicle system to the optimum setting; and/or (4) causing the recommendation, via the one or more processors, to be presented to a driver of the vehicle having the autonomous or semi-autonomous vehicle system to facilitate the driver changing the actual setting to the optimum setting (or otherwise accepting, or being notified of, an automatic change to the optimum setting). The method may include additional, less, or alternate functionality, including that discussed elsewhere herein.
  • For instance, the method may include generating or adjusting, via the one or more processors, a premium, rate, discount, or reward of an auto insurance policy for the vehicle based upon the vehicle having or being equipped with the recommendation functionality associated with recommending optimum settings, and/or a percentage of the driver accepting the recommendations provided. The optimum setting that is determined may be determined based upon a setting for the autonomous or semi-autonomous vehicle technology that reduces a likelihood of the vehicle employing the technology having, or being involved in, a vehicle accident or collision. Additionally or alternatively, the optimum setting that is determined may be determined based upon risk associated with the autonomous or semi-autonomous vehicle technology.
  • In another aspect, a computer-implemented method of determining accident fault may be provided. The method may include (1) receiving or collecting, at or via a remote server (or processor) associated with an insurance provider, performance data associated with autonomous or semi-autonomous vehicle technology for an insured vehicle involved in a vehicle accident, the insured vehicle being insured by an insurance policy issued by the insurance provider; (2) analyzing, at or via the remote server, the performance data received; (3) determining, at or via the remote server, from analysis of the performance data: (a) a first percentage of fault of the vehicle accident for the autonomous or semi-autonomous vehicle technology of the insured vehicle in operation at a time of the vehicle accident; and/or (b) a second percentage of fault of the vehicle accident for an insured driver who was driving the insured vehicle during the vehicle accident; and/or (4) adjusting or updating, at or via the remote server, a premium, rate, discount, or reward of the insurance policy covering the insured vehicle based upon the first and second percentages of fault that are assigned to the insured vehicle and insured driver, respectively, from the analysis of the performance data. The method may include additional, less, or alternate functionality, including that discussed elsewhere herein.
  • For instance, the method may include further determining, at or via the remote server, from analysis of the performance data: (c) a third percentage of fault of the accident for other vehicles or drivers involved in and/or causing the vehicle accident. The method may include handling, at or via the remote server, an insurance claim for the vehicle accident submitted by the insured using, or based upon, the performance data received associated with the autonomous or semi-autonomous vehicle functionality exhibited during the vehicle accident.
  • The method may include changing, at or via the remote server, policy coverages (and/or premiums, rates, discounts, etc.) for the insured or the insured vehicle using, or based upon, the performance data received associated with the autonomous or semi-autonomous vehicle functionality exhibited during the vehicle accident. Additionally or alternatively, the method may include changing, at or via the remote server, liability limits or coverages (and/or premiums, rates, discounts, etc.) for the insured or the insured vehicle using, or based upon, the performance data received associated with the autonomous or semi-autonomous vehicle functionality exhibited during the vehicle accident.
  • While the preferred embodiments of the invention have been described, it should be understood that the invention is not so limited and modifications may be made without departing from the invention. The scope of the invention is defined by the appended claims, and all devices that come within the meaning of the claims, either literally or by equivalence, are intended to be embraced therein.
  • It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention.
  • Other Matters
  • Although the text herein sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the invention is defined by the words of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. One could implement numerous alternate embodiments, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.
  • It should also be understood that, unless a term is expressly defined in this patent using the sentence “As used herein, the term ‘ ’ is hereby defined to mean . . . ” or a similar sentence, there is no intent to limit the meaning of that term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be interpreted to be limited in scope based upon any statement made in any section of this patent (other than the language of the claims). To the extent that any term recited in the claims at the end of this disclosure is referred to in this disclosure in a manner consistent with a single meaning, that is done for sake of clarity only so as to not confuse the reader, and it is not intended that such claim term be limited, by implication or otherwise, to that single meaning. Finally, unless a claim element is defined by reciting the word “means” and a function without the recital of any structure, it is not intended that the scope of any claim element be interpreted based upon the application of 35 U.S.C. § 112(f).
  • Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.
  • Additionally, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (code embodied on a non-transitory, tangible machine-readable medium) or hardware. In hardware, the routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.
  • In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
  • Accordingly, the term “hardware” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware is temporarily configured (e.g., programmed), the hardware need not be configured or instantiated at any one instance in time. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. Hardware elements can provide information to, and receive information from, other hardware elements. Accordingly, the described hardware may be regarded as being communicatively coupled.
  • The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. Similarly, the methods or routines described herein may be at least partially processor-implemented. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.
  • Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information. As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. For example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context.
  • As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the description. In this description, and the claims that follow, the singular also includes the plural unless it is obvious that it is meant otherwise. This detailed description is to be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. One could implement numerous alternate embodiments, using either current technology or technology developed after the filing date of this application.
  • Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for system and a method for assigning mobile device data to a vehicle through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.
  • The particular features, structures, or characteristics of any specific embodiment may be combined in any suitable manner and in any suitable combination with one or more other embodiments, including the use of selected features without corresponding use of other features. In addition, many modifications may be made to adapt a particular application, situation or material to the essential scope and spirit of the present invention. It is to be understood that other variations and modifications of the embodiments of the present invention described and illustrated herein are possible in light of the teachings herein and are to be considered part of the spirit and scope of the present invention.

Claims (21)

1-20. (canceled)
21. A computer-implemented method for determining fault relating to a collision or other loss, comprising:
receiving, at one or more processors, operating data regarding operation of a vehicle during a collision, wherein the operating data includes sensor data from one or more vehicle sensors and information regarding operation of one or more autonomous operation features of the vehicle, including control signals generated by the one or more autonomous operation features to control the vehicle;
receiving, at one or more processors, information regarding use levels of the one or more autonomous operation features during the collision, wherein the use levels indicate a value of at least one variable setting of at least one of the one or more autonomous operation features enabled during the collision; and
automatically determining, by one or more processors and without human involvement, an allocation of fault for the collision using the received operating data and the use levels of the one or more autonomous operation features by (i) determining that the use levels indicate the one or more autonomous operation features were configured and operating to control an aspect of vehicle operation during the collision, and (ii) determining, by comparing intended control actions of the one or more autonomous operation features indicated by the control signals against observed operation of the vehicle indicated by the sensor data, relative fault between the one or more autonomous operation features and other control components configured to implement control actions for the vehicle based upon the control signals.
22. The computer-implemented method of claim 21, wherein determining the allocation of fault includes allocating, by one or more processors, fault for the collision between one or more of the following: a vehicle operator, the one or more autonomous operation features as a group, each of the one or more autonomous operation features separately, or a third party.
23. The computer-implemented method of claim 21, further comprising:
determining, by one or more processors, an adjustment to a cost associated with a vehicle insurance policy based at least in part upon the allocation of fault when the at least a portion of the fault is determined to be allocated to a vehicle operator.
24. The computer-implemented method of claim 23, wherein the cost associated with the vehicle insurance policy includes at least one of the following a premium, a rate, or a rate category.
25. The computer-implemented method of claim 21, further comprising:
determining, by one or more processors, an adjustment to a risk level associated with the one or more autonomous operation features based at least in part upon the allocation of fault when the at least a portion of the fault is determined to be allocated to the one or more autonomous operation features.
26. The computer-implemented method of claim 21, wherein determining the allocation of fault further includes determining, by one or more processors, one or more of the following: a point of impact on the vehicle, a point of impact on one or more additional vehicles, a velocity of the vehicle, a velocity of one or more additional vehicles, a movement of the vehicle, a movement of one or more additional vehicles, a location of one or more obstructions, a movement of one or more obstructions, a location of one or more pedestrians, a movement of one or more pedestrians, a measure of road surface integrity, a measure of road surface friction, a location of one or more traffic signs, a location of one or more traffic signals, or an indication of a state of one or more traffic signals.
27. The computer-implemented method of claim 21, wherein determining the allocation of fault further includes determining, by one or more processors, a control signal generated by one or more autonomous operation features of the vehicle.
28. The computer-implemented method of claim 21, wherein determining the allocation of fault further includes determining, by one or more processors, a control signal generated by one or more autonomous operation features of one or more additional vehicles.
29. The computer-implemented method of claim 21, further comprising:
presenting, via a display, the automatically determined allocation of fault to a human reviewer.
30. The computer-implemented method of claim 21 wherein the operating data includes communication data from external sources.
31. A computer system for determining fault relating to a collision or other loss, comprising:
one or more processors;
one or more communication modules adapted to communicate data; and
a non-transitory program memory coupled to the one or more processors and storing executable instructions that when executed by the one or more processors cause the computer system to:
receive operating data regarding operation of a vehicle during a collision, wherein the operating data includes sensor data from one or more vehicle sensors and information regarding operation of one or more autonomous operation features of the vehicle, including control signals generated by the one or more autonomous operation features to control the vehicle;
receive information regarding use levels of the one or more autonomous operation features during the collision, wherein the use levels indicate a value of at least one variable setting of at least one of the one or more autonomous operation features enabled during the collision; and
automatically determine, without human involvement an allocation of fault for the collision using the received operating data and the use levels of the one or more autonomous operation features by (i) determining whether the use levels indicate the one or more autonomous operation features were configured and operating to control an aspect of vehicle operation during the collision, and (ii) determining, by comparing intended control actions of the one or more autonomous operation features indicated by control signals against observed operation of the vehicle indicated by the sensor data, relative fault between the one or more autonomous operation features and other control components configured to implement control actions for the vehicle based upon the control signals.
32. The computer system of claim 31, wherein instructions that cause the computer system to determine the allocation of fault further cause the computer system to allocate fault for the collision between one or more of the following: a vehicle operator, the one or more autonomous operation features as a group, each of the one or more autonomous operation features separately, or a third party.
33. The computer system of claim 31, wherein the instructions further cause the computer system to:
determine an adjustment to a cost associated with a vehicle insurance policy based upon the allocation of fault when the at least a portion of the fault is determined to be allocated to a vehicle operator.
34. The computer system of claim 31, wherein the instructions further cause the computer system to:
determine an adjustment to a risk level associated with the one or more autonomous operation features based at least in part upon the allocation of fault when the at least a portion of the fault is determined to be allocated to the one or more autonomous operation features.
35. The computer system of claim 31, wherein the instructions further cause the computer system to:
present the automatically determined allocation of fault to a human reviewer.
36. The computer system of claim 31, wherein the instructions that cause the computer system to determine the allocation of fault further include instructions that cause the computer system to determine one or more of the following: a point of impact on the vehicle, a point of impact on one or more additional vehicles, a velocity of the vehicle, a velocity of one or more additional vehicles, a movement of the vehicle, a movement of one or more additional vehicles, a location of one or more obstructions, a movement of one or more obstructions, a location of one or more pedestrians, a movement of one or more pedestrians, a measure of road surface integrity, a measure of road surface friction, a location of one or more traffic signs, a location of one or more traffic signals, or an indication of a state of one or more traffic signals.
37. The computer system of claim 31, wherein the instructions that cause the computer system to determine the allocation of fault further include instructions that cause the computer system to determine one or more of the following: a control signal generated by one or more autonomous operation features of the vehicle, or a control signal generated by one or more autonomous operation features of one or more additional vehicles.
38. A tangible, non-transitory computer-readable medium storing instructions for determining fault relating to a collision or other loss that when executed by at least one processor of a computer system, cause the computer system to:
receive operating data regarding operation of the vehicle during a collision, wherein the operating data includes sensor data from one or more vehicle sensors and information regarding operation of one or more autonomous operation features of the vehicle, including control signals generated by the one or more autonomous operation features to control the vehicle;
receive information regarding use levels of the one or more autonomous operation features during the collision, wherein the use levels indicate a value of at least one variable setting of at least one of the one or more autonomous operation features enabled during the collision; and
automatically determine, without human involvement an allocation of fault for the collision using the received operating data and the use levels of the one or more autonomous operation features by (i) determining whether the use levels indicate the one or more autonomous operation features were configured and operating to control an aspect of vehicle operation during the collision, and (ii) determining, by comparing intended control actions of the one or more autonomous operation features indicated by the control signals against observed operation of the vehicle indicated by the sensor data, relative fault between the one or more autonomous operation features and other control components configured to implement control actions for the vehicle based upon the control signals.
39. The tangible, non-transitory computer-readable medium of claim 38, further comprising instructions that, when executed by at least one processor of a computer system, cause the computer system to determine the allocation of fault further cause the computer system to allocate fault for the collision between one or more of the following: a vehicle operator, the one or more autonomous operation features as a group, each of the one or more autonomous operation features separately, or a third party.
40. The tangible, non-transitory computer-readable medium of claim 38, further comprising instructions that, when executed by at least one processor of a computer system, cause the computer system to:
determine an adjustment to a vehicle insurance policy based upon the allocation of fault when the at least a portion of the fault is determined to be allocated to a vehicle operator.
US16/895,408 2014-05-20 2020-06-08 Accident fault determination for autonomous vehicles Pending US20200302548A1 (en)

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US16/190,795 US10726499B1 (en) 2014-05-20 2018-11-14 Accident fault determination for autonomous vehicles
US16/895,408 US20200302548A1 (en) 2014-05-20 2020-06-08 Accident fault determination for autonomous vehicles

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US15/410,192 Active US10467704B1 (en) 2014-05-20 2017-01-19 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US15/472,813 Active US10043323B1 (en) 2014-05-20 2017-03-29 Accident response using autonomous vehicle monitoring
US15/627,596 Active 2038-01-07 US11127083B1 (en) 2014-05-20 2017-06-20 Driver feedback alerts based upon monitoring use of autonomous vehicle operation features
US15/689,374 Active 2036-02-15 US10685403B1 (en) 2014-05-20 2017-08-29 Fault determination with autonomous feature use monitoring
US15/689,437 Active 2037-09-27 US11062395B1 (en) 2014-05-20 2017-08-29 Accident fault determination for autonomous vehicles
US15/806,784 Active US10510123B1 (en) 2014-05-20 2017-11-08 Accident risk model determination using autonomous vehicle operating data
US15/806,789 Active 2036-02-23 US10748218B2 (en) 2014-05-20 2017-11-08 Autonomous vehicle technology effectiveness determination for insurance pricing
US15/976,971 Active US10504306B1 (en) 2014-05-20 2018-05-11 Accident response using autonomous vehicle monitoring
US16/017,317 Active 2035-09-18 US11062396B1 (en) 2014-05-20 2018-06-25 Determining autonomous vehicle technology performance for insurance pricing and offering
US16/190,765 Active US10726498B1 (en) 2014-05-20 2018-11-14 Accident fault determination for autonomous vehicles
US16/190,795 Active US10726499B1 (en) 2014-05-20 2018-11-14 Accident fault determination for autonomous vehicles
US16/201,100 Active US10963969B1 (en) 2014-05-20 2018-11-27 Autonomous communication feature use and insurance pricing
US16/212,854 Active US11023629B1 (en) 2014-05-20 2018-12-07 Autonomous vehicle operation feature evaluation
US16/393,184 Active US10719885B1 (en) 2014-05-20 2019-04-24 Autonomous feature use monitoring and insurance pricing
US16/522,179 Active 2035-09-16 US11100591B1 (en) 2014-05-20 2019-07-25 Autonomous vehicle operation feature usage recommendations
US16/580,076 Active 2036-01-09 US11348182B1 (en) 2014-05-20 2019-09-24 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US16/672,868 Active US11062399B1 (en) 2014-05-20 2019-11-04 Accident response using autonomous vehicle monitoring
US16/676,563 Active US11238538B1 (en) 2014-05-20 2019-11-07 Accident risk model determination using autonomous vehicle operating data
US16/685,319 Active 2035-09-22 US11288751B1 (en) 2014-05-20 2019-11-15 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US16/848,048 Active US11010840B1 (en) 2014-05-20 2020-04-14 Fault determination with autonomous feature use monitoring
US16/894,328 Active US10977741B2 (en) 2014-05-20 2020-06-05 Autonomous feature use monitoring and insurance pricing
US16/895,330 Active US11127086B2 (en) 2014-05-20 2020-06-08 Accident fault determination for autonomous vehicles
US16/895,373 Active US11080794B2 (en) 2014-05-20 2020-06-08 Autonomous vehicle technology effectiveness determination for insurance pricing
US16/895,408 Pending US20200302548A1 (en) 2014-05-20 2020-06-08 Accident fault determination for autonomous vehicles
US17/088,806 Active 2036-01-21 US11710188B2 (en) 2014-05-20 2020-11-04 Autonomous communication feature use and insurance pricing
US17/235,620 Active 2035-07-06 US11436685B1 (en) 2014-05-20 2021-04-20 Fault determination with autonomous feature use monitoring
US17/679,452 Active US11869092B2 (en) 2014-05-20 2022-02-24 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US17/888,703 Pending US20220391992A1 (en) 2014-05-20 2022-08-16 Fault determination with autonomous feature use monitoring
US18/215,690 Pending US20230334585A1 (en) 2014-05-20 2023-06-28 Autonomous communication feature use and insurance pricing

Family Applications Before (41)

Application Number Title Priority Date Filing Date
US14/713,230 Active 2035-07-19 US10185997B1 (en) 2014-05-20 2015-05-15 Accident fault determination for autonomous vehicles
US14/713,206 Active US10055794B1 (en) 2014-05-20 2015-05-15 Determining autonomous vehicle technology performance for insurance pricing and offering
US14/713,237 Active US9858621B1 (en) 2014-05-20 2015-05-15 Autonomous vehicle technology effectiveness determination for insurance pricing
US14/713,240 Active US9792656B1 (en) 2014-05-20 2015-05-15 Fault determination with autonomous feature use monitoring
US14/713,194 Active US10181161B1 (en) 2014-05-20 2015-05-15 Autonomous communication feature use
US14/713,214 Active 2035-06-17 US9852475B1 (en) 2014-05-20 2015-05-15 Accident risk model determination using autonomous vehicle operating data
US14/713,271 Active US10089693B1 (en) 2014-05-20 2015-05-15 Fully autonomous vehicle insurance pricing
US14/713,249 Active 2035-10-03 US10529027B1 (en) 2014-05-20 2015-05-15 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US14/713,223 Active US9767516B1 (en) 2014-05-20 2015-05-15 Driver feedback alerts based upon monitoring use of autonomous vehicle
US14/713,184 Active US10026130B1 (en) 2014-05-20 2015-05-15 Autonomous vehicle collision risk assessment
US14/713,217 Abandoned US20210133871A1 (en) 2014-05-20 2015-05-15 Autonomous vehicle operation feature usage recommendations
US14/713,261 Active US9805423B1 (en) 2014-05-20 2015-05-15 Accident fault determination for autonomous vehicles
US14/713,188 Active 2035-11-16 US10354330B1 (en) 2014-05-20 2015-05-15 Autonomous feature use monitoring and insurance pricing
US14/713,201 Active US9715711B1 (en) 2014-05-20 2015-05-15 Autonomous vehicle insurance pricing and offering based upon accident risk
US14/713,254 Active 2035-07-02 US10185998B1 (en) 2014-05-20 2015-05-15 Accident fault determination for autonomous vehicles
US14/713,266 Active US9754325B1 (en) 2014-05-20 2015-05-15 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US14/713,226 Active US9646428B1 (en) 2014-05-20 2015-05-15 Accident response using autonomous vehicle monitoring
US14/713,244 Active 2037-05-18 US10223479B1 (en) 2014-05-20 2015-05-15 Autonomous vehicle operation feature evaluation
US15/410,192 Active US10467704B1 (en) 2014-05-20 2017-01-19 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US15/472,813 Active US10043323B1 (en) 2014-05-20 2017-03-29 Accident response using autonomous vehicle monitoring
US15/627,596 Active 2038-01-07 US11127083B1 (en) 2014-05-20 2017-06-20 Driver feedback alerts based upon monitoring use of autonomous vehicle operation features
US15/689,374 Active 2036-02-15 US10685403B1 (en) 2014-05-20 2017-08-29 Fault determination with autonomous feature use monitoring
US15/689,437 Active 2037-09-27 US11062395B1 (en) 2014-05-20 2017-08-29 Accident fault determination for autonomous vehicles
US15/806,784 Active US10510123B1 (en) 2014-05-20 2017-11-08 Accident risk model determination using autonomous vehicle operating data
US15/806,789 Active 2036-02-23 US10748218B2 (en) 2014-05-20 2017-11-08 Autonomous vehicle technology effectiveness determination for insurance pricing
US15/976,971 Active US10504306B1 (en) 2014-05-20 2018-05-11 Accident response using autonomous vehicle monitoring
US16/017,317 Active 2035-09-18 US11062396B1 (en) 2014-05-20 2018-06-25 Determining autonomous vehicle technology performance for insurance pricing and offering
US16/190,765 Active US10726498B1 (en) 2014-05-20 2018-11-14 Accident fault determination for autonomous vehicles
US16/190,795 Active US10726499B1 (en) 2014-05-20 2018-11-14 Accident fault determination for autonomous vehicles
US16/201,100 Active US10963969B1 (en) 2014-05-20 2018-11-27 Autonomous communication feature use and insurance pricing
US16/212,854 Active US11023629B1 (en) 2014-05-20 2018-12-07 Autonomous vehicle operation feature evaluation
US16/393,184 Active US10719885B1 (en) 2014-05-20 2019-04-24 Autonomous feature use monitoring and insurance pricing
US16/522,179 Active 2035-09-16 US11100591B1 (en) 2014-05-20 2019-07-25 Autonomous vehicle operation feature usage recommendations
US16/580,076 Active 2036-01-09 US11348182B1 (en) 2014-05-20 2019-09-24 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US16/672,868 Active US11062399B1 (en) 2014-05-20 2019-11-04 Accident response using autonomous vehicle monitoring
US16/676,563 Active US11238538B1 (en) 2014-05-20 2019-11-07 Accident risk model determination using autonomous vehicle operating data
US16/685,319 Active 2035-09-22 US11288751B1 (en) 2014-05-20 2019-11-15 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US16/848,048 Active US11010840B1 (en) 2014-05-20 2020-04-14 Fault determination with autonomous feature use monitoring
US16/894,328 Active US10977741B2 (en) 2014-05-20 2020-06-05 Autonomous feature use monitoring and insurance pricing
US16/895,330 Active US11127086B2 (en) 2014-05-20 2020-06-08 Accident fault determination for autonomous vehicles
US16/895,373 Active US11080794B2 (en) 2014-05-20 2020-06-08 Autonomous vehicle technology effectiveness determination for insurance pricing

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US17/679,452 Active US11869092B2 (en) 2014-05-20 2022-02-24 Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US17/888,703 Pending US20220391992A1 (en) 2014-05-20 2022-08-16 Fault determination with autonomous feature use monitoring
US18/215,690 Pending US20230334585A1 (en) 2014-05-20 2023-06-28 Autonomous communication feature use and insurance pricing

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Families Citing this family (505)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10878646B2 (en) 2005-12-08 2020-12-29 Smartdrive Systems, Inc. Vehicle event recorder systems
US8996240B2 (en) 2006-03-16 2015-03-31 Smartdrive Systems, Inc. Vehicle event recorders with integrated web server
US8649933B2 (en) 2006-11-07 2014-02-11 Smartdrive Systems Inc. Power management systems for automotive video event recorders
US8989959B2 (en) 2006-11-07 2015-03-24 Smartdrive Systems, Inc. Vehicle operator performance history recording, scoring and reporting systems
US8868288B2 (en) 2006-11-09 2014-10-21 Smartdrive Systems, Inc. Vehicle exception event management systems
US8239092B2 (en) 2007-05-08 2012-08-07 Smartdrive Systems Inc. Distributed vehicle event recorder systems having a portable memory data transfer system
US10157422B2 (en) 2007-05-10 2018-12-18 Allstate Insurance Company Road segment safety rating
US10096038B2 (en) 2007-05-10 2018-10-09 Allstate Insurance Company Road segment safety rating system
US8606512B1 (en) 2007-05-10 2013-12-10 Allstate Insurance Company Route risk mitigation
US9932033B2 (en) 2007-05-10 2018-04-03 Allstate Insurance Company Route risk mitigation
US10254764B2 (en) 2016-05-31 2019-04-09 Peloton Technology, Inc. Platoon controller state machine
US9582006B2 (en) 2011-07-06 2017-02-28 Peloton Technology, Inc. Systems and methods for semi-autonomous convoying of vehicles
US20170242443A1 (en) 2015-11-02 2017-08-24 Peloton Technology, Inc. Gap measurement for vehicle convoying
US10520581B2 (en) 2011-07-06 2019-12-31 Peloton Technology, Inc. Sensor fusion for autonomous or partially autonomous vehicle control
US10657597B1 (en) 2012-02-17 2020-05-19 United Services Automobile Association (Usaa) Systems and methods for dynamic insurance premiums
US9424696B2 (en) 2012-10-04 2016-08-23 Zonar Systems, Inc. Virtual trainer for in vehicle driver coaching and to collect metrics to improve driver performance
US10713726B1 (en) 2013-01-13 2020-07-14 United Services Automobile Association (Usaa) Determining insurance policy modifications using informatic sensor data
US10154382B2 (en) 2013-03-12 2018-12-11 Zendrive, Inc. System and method for determining a driver in a telematic application
US20180210463A1 (en) 2013-03-15 2018-07-26 Peloton Technology, Inc. System and method for implementing pre-cognition braking and/or avoiding or mitigation risks among platooning vehicles
EP2972096B1 (en) * 2013-03-15 2019-01-09 Volkswagen Aktiengesellschaft Automatic driving route planning application
US11294396B2 (en) 2013-03-15 2022-04-05 Peloton Technology, Inc. System and method for implementing pre-cognition braking and/or avoiding or mitigation risks among platooning vehicles
US9710858B1 (en) 2013-08-16 2017-07-18 United Services Automobile Association (Usaa) Insurance policy alterations using informatic sensor data
US10169821B2 (en) * 2013-09-20 2019-01-01 Elwha Llc Systems and methods for insurance based upon status of vehicle software
US9501878B2 (en) 2013-10-16 2016-11-22 Smartdrive Systems, Inc. Vehicle event playback apparatus and methods
US9610955B2 (en) 2013-11-11 2017-04-04 Smartdrive Systems, Inc. Vehicle fuel consumption monitor and feedback systems
US11416941B1 (en) 2014-01-10 2022-08-16 United Services Automobile Association (Usaa) Electronic sensor management
US10552911B1 (en) 2014-01-10 2020-02-04 United Services Automobile Association (Usaa) Determining status of building modifications using informatics sensor data
US11087404B1 (en) 2014-01-10 2021-08-10 United Services Automobile Association (Usaa) Electronic sensor management
US10096067B1 (en) 2014-01-24 2018-10-09 Allstate Insurance Company Reward system related to a vehicle-to-vehicle communication system
US9390451B1 (en) 2014-01-24 2016-07-12 Allstate Insurance Company Insurance system related to a vehicle-to-vehicle communication system
US9355423B1 (en) 2014-01-24 2016-05-31 Allstate Insurance Company Reward system related to a vehicle-to-vehicle communication system
US10796369B1 (en) 2014-02-19 2020-10-06 Allstate Insurance Company Determining a property of an insurance policy based on the level of autonomy of a vehicle
US10783587B1 (en) 2014-02-19 2020-09-22 Allstate Insurance Company Determining a driver score based on the driver's response to autonomous features of a vehicle
US10803525B1 (en) 2014-02-19 2020-10-13 Allstate Insurance Company Determining a property of an insurance policy based on the autonomous features of a vehicle
US9940676B1 (en) 2014-02-19 2018-04-10 Allstate Insurance Company Insurance system for analysis of autonomous driving
US10783586B1 (en) 2014-02-19 2020-09-22 Allstate Insurance Company Determining a property of an insurance policy based on the density of vehicles
US8892310B1 (en) 2014-02-21 2014-11-18 Smartdrive Systems, Inc. System and method to detect execution of driving maneuvers
US11847666B1 (en) 2014-02-24 2023-12-19 United Services Automobile Association (Usaa) Determining status of building modifications using informatics sensor data
JP6252252B2 (en) * 2014-02-28 2017-12-27 株式会社デンソー Automatic driving device
US10614525B1 (en) 2014-03-05 2020-04-07 United Services Automobile Association (Usaa) Utilizing credit and informatic data for insurance underwriting purposes
US10185997B1 (en) 2014-05-20 2019-01-22 State Farm Mutual Automobile Insurance Company Accident fault determination for autonomous vehicles
US9972054B1 (en) 2014-05-20 2018-05-15 State Farm Mutual Automobile Insurance Company Accident fault determination for autonomous vehicles
US10373259B1 (en) 2014-05-20 2019-08-06 State Farm Mutual Automobile Insurance Company Fully autonomous vehicle insurance pricing
US10599155B1 (en) 2014-05-20 2020-03-24 State Farm Mutual Automobile Insurance Company Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US11669090B2 (en) 2014-05-20 2023-06-06 State Farm Mutual Automobile Insurance Company Autonomous vehicle operation feature monitoring and evaluation of effectiveness
US10475127B1 (en) 2014-07-21 2019-11-12 State Farm Mutual Automobile Insurance Company Methods of providing insurance savings based upon telematics and insurance incentives
US9628565B2 (en) * 2014-07-23 2017-04-18 Here Global B.V. Highly assisted driving platform
US10366456B2 (en) 2014-08-29 2019-07-30 Guidewire Software, Inc. Operational data corresponding to a product model
US10145684B1 (en) 2014-09-22 2018-12-04 State Farm Mutual Automobile Insurance Company Accident reconstruction implementing unmanned aerial vehicles (UAVs)
US10991049B1 (en) 2014-09-23 2021-04-27 United Services Automobile Association (Usaa) Systems and methods for acquiring insurance related informatics
DE102014219408A1 (en) * 2014-09-25 2016-04-14 Volkswagen Aktiengesellschaft Method and device for setting a thermal comfort state
WO2016046831A1 (en) * 2014-09-26 2016-03-31 Natan Tomer Methods and systems of managing parking space occupancy
US10664920B1 (en) 2014-10-06 2020-05-26 State Farm Mutual Automobile Insurance Company Blockchain systems and methods for providing insurance coverage to affinity groups
US11574368B1 (en) 2014-10-06 2023-02-07 State Farm Mutual Automobile Insurance Company Risk mitigation for affinity groupings
US10817949B1 (en) 2014-10-06 2020-10-27 State Farm Mutual Automobile Insurance Company Medical diagnostic-initiated insurance offering
US20210166320A1 (en) 2014-10-06 2021-06-03 State Farm Mutual Automobile Insurance Company System and method for obtaining and/or maintaining insurance coverage
US11069257B2 (en) 2014-11-13 2021-07-20 Smartdrive Systems, Inc. System and method for detecting a vehicle event and generating review criteria
US10915965B1 (en) 2014-11-13 2021-02-09 State Farm Mutual Automobile Insurance Company Autonomous vehicle insurance based upon usage
EP3223256B1 (en) * 2014-11-17 2021-11-17 Hitachi Astemo, Ltd. Automatic driving system
US11855768B2 (en) 2014-12-29 2023-12-26 Guidewire Software, Inc. Disaster scenario based inferential analysis using feedback for extracting and combining cyber risk information
US10050990B2 (en) 2014-12-29 2018-08-14 Guidewire Software, Inc. Disaster scenario based inferential analysis using feedback for extracting and combining cyber risk information
US9699209B2 (en) 2014-12-29 2017-07-04 Cyence Inc. Cyber vulnerability scan analyses with actionable feedback
WO2017078986A1 (en) * 2014-12-29 2017-05-11 Cyence Inc. Diversity analysis with actionable feedback methodologies
US10050989B2 (en) 2014-12-29 2018-08-14 Guidewire Software, Inc. Inferential analysis using feedback for extracting and combining cyber risk information including proxy connection analyses
US11863590B2 (en) 2014-12-29 2024-01-02 Guidewire Software, Inc. Inferential analysis using feedback for extracting and combining cyber risk information
US10341376B2 (en) 2014-12-29 2019-07-02 Guidewire Software, Inc. Diversity analysis with actionable feedback methodologies
JP6773024B2 (en) * 2015-03-06 2020-10-21 ソニー株式会社 Recording device, recording method and computer program
DE102015103773A1 (en) * 2015-03-16 2016-09-22 Valeo Schalter Und Sensoren Gmbh Method for operating a communication device for a motor vehicle during an autonomous driving mode, communication device and motor vehicle
US10404748B2 (en) 2015-03-31 2019-09-03 Guidewire Software, Inc. Cyber risk analysis and remediation using network monitored sensors and methods of use
US9679420B2 (en) * 2015-04-01 2017-06-13 Smartdrive Systems, Inc. Vehicle event recording system and method
US10187766B2 (en) * 2015-04-09 2019-01-22 Lg Electronics Inc. Method and apparatus for gathering location information of vehicle user equipment in a wireless access system supporting V2X services
JP6052530B1 (en) * 2015-04-21 2016-12-27 パナソニックIpマネジメント株式会社 Information processing system, information processing method, and program
JP6193912B2 (en) * 2015-04-24 2017-09-06 株式会社パイ・アール Drive recorder
US10077056B1 (en) 2015-04-24 2018-09-18 State Farm Mutual Automobile Insurance Company Managing self-driving behavior of autonomous or semi-autonomous vehicle based upon actual driving behavior of driver
US9672719B1 (en) * 2015-04-27 2017-06-06 State Farm Mutual Automobile Insurance Company Device for automatic crash notification
US10489863B1 (en) * 2015-05-27 2019-11-26 United Services Automobile Association (Usaa) Roof inspection systems and methods
US10536357B2 (en) * 2015-06-05 2020-01-14 Cisco Technology, Inc. Late data detection in data center
US9836895B1 (en) * 2015-06-19 2017-12-05 Waymo Llc Simulating virtual objects
US10131362B1 (en) * 2015-06-23 2018-11-20 United Services Automobile Association (Usaa) Automobile detection system
CN105116817A (en) * 2015-06-26 2015-12-02 小米科技有限责任公司 Balance car management method and device
DE102015212313A1 (en) * 2015-07-01 2017-01-05 Robert Bosch Gmbh Concept for transferring a vehicle from a start position to a destination position
DE102016210848A1 (en) * 2015-07-06 2017-01-12 Ford Global Technologies, Llc Method for avoiding a collision of a vehicle with an object, and driving assistance system
US10940868B2 (en) * 2015-07-10 2021-03-09 Honda Motor Co., Ltd. Vehicle control device, vehicle control method, and vehicle control program
US9457754B1 (en) * 2015-07-13 2016-10-04 State Farm Mutual Automobile Insurance Company Method and system for identifying vehicle collisions using sensor data
US10214206B2 (en) * 2015-07-13 2019-02-26 Magna Electronics Inc. Parking assist system for vehicle
DE102016008987B4 (en) * 2015-07-31 2021-09-16 Fanuc Corporation Machine learning method and machine learning apparatus for learning failure conditions, and failure prediction apparatus and failure prediction system including the machine learning apparatus
US9734721B2 (en) * 2015-08-14 2017-08-15 Here Global B.V. Accident notifications
US9818239B2 (en) 2015-08-20 2017-11-14 Zendrive, Inc. Method for smartphone-based accident detection
ES2904564T3 (en) 2015-08-20 2022-04-05 Zendrive Inc Accelerometer assisted navigation method
US20210272207A1 (en) 2015-08-28 2021-09-02 State Farm Mutual Automobile Insurance Company Vehicular driver profiles and discounts
US20170061812A1 (en) * 2015-09-01 2017-03-02 Karz Software Technologies Ltd. Driver monitoring and feedback system
JP6697702B2 (en) * 2015-09-10 2020-05-27 パナソニックIpマネジメント株式会社 Automatic stop device and automatic stop method
US10462689B2 (en) * 2015-09-22 2019-10-29 Veniam, Inc. Systems and methods for monitoring a network of moving things
US10139828B2 (en) * 2015-09-24 2018-11-27 Uber Technologies, Inc. Autonomous vehicle operated with safety augmentation
JP6567376B2 (en) 2015-09-25 2019-08-28 パナソニック株式会社 apparatus
US11436911B2 (en) 2015-09-30 2022-09-06 Johnson Controls Tyco IP Holdings LLP Sensor based system and method for premises safety and operational profiling based on drift analysis
US11151654B2 (en) 2015-09-30 2021-10-19 Johnson Controls Tyco IP Holdings LLP System and method for determining risk profile, adjusting insurance premiums and automatically collecting premiums based on sensor data
CN108137050B (en) * 2015-09-30 2021-08-10 索尼公司 Driving control device and driving control method
US10902524B2 (en) 2015-09-30 2021-01-26 Sensormatic Electronics, LLC Sensor based system and method for augmenting underwriting of insurance policies
US10354332B2 (en) * 2015-09-30 2019-07-16 Sensormatic Electronics, LLC Sensor based system and method for drift analysis to predict equipment failure
US11127082B1 (en) * 2015-10-12 2021-09-21 Allstate Insurance Company Virtual assistant for recommendations on whether to arbitrate claims
US10698404B2 (en) * 2015-10-20 2020-06-30 Ford Global Technologies, Llc Facilitating lane-splitting by motorcycles
EP3159853B1 (en) * 2015-10-23 2019-03-27 Harman International Industries, Incorporated Systems and methods for advanced driver assistance analytics
US10460534B1 (en) * 2015-10-26 2019-10-29 Allstate Insurance Company Vehicle-to-vehicle accident detection
US10401852B2 (en) 2015-11-04 2019-09-03 Zoox, Inc. Teleoperation system and method for trajectory modification of autonomous vehicles
WO2017079341A2 (en) 2015-11-04 2017-05-11 Zoox, Inc. Automated extraction of semantic information to enhance incremental mapping modifications for robotic vehicles
US9632502B1 (en) * 2015-11-04 2017-04-25 Zoox, Inc. Machine-learning systems and techniques to optimize teleoperation and/or planner decisions
US9606539B1 (en) 2015-11-04 2017-03-28 Zoox, Inc. Autonomous vehicle fleet service and system
US9630619B1 (en) 2015-11-04 2017-04-25 Zoox, Inc. Robotic vehicle active safety systems and methods
US11024158B2 (en) * 2015-11-04 2021-06-01 Teleefonaktiebolaget Lm Ericsson (Publ) Method of providing traffic related information and device, computer program and computer program product
US11283877B2 (en) 2015-11-04 2022-03-22 Zoox, Inc. Software application and logic to modify configuration of an autonomous vehicle
DE102016220670A1 (en) * 2015-11-06 2017-05-11 Ford Global Technologies, Llc Method and system for testing software for autonomous vehicles
DE102016220913A1 (en) * 2015-11-06 2017-05-11 Ford Global Technologies, Llc Method and device for generating test cases for autonomous vehicles
US10176525B2 (en) * 2015-11-09 2019-01-08 International Business Machines Corporation Dynamically adjusting insurance policy parameters for a self-driving vehicle
JP6392734B2 (en) * 2015-11-27 2018-09-19 株式会社Subaru Information processing apparatus, vehicle information processing apparatus, information processing method, and vehicle information processing method
US20170161289A1 (en) * 2015-12-08 2017-06-08 Hartford Fire Insurance Company System to improve data exchange using advanced data analytics
DE102015224696A1 (en) * 2015-12-09 2017-06-14 Robert Bosch Gmbh Risk-based control of a motor vehicle
SE539283C8 (en) * 2015-12-15 2017-07-18 Greater Than S A Method and system for assessing the trip performance of a driver
CN109415033B (en) * 2015-12-24 2022-03-04 北京嘀嘀无限科技发展有限公司 System and method for vehicle management
US10324463B1 (en) 2016-01-22 2019-06-18 State Farm Mutual Automobile Insurance Company Autonomous vehicle operation adjustment based upon route
US11242051B1 (en) 2016-01-22 2022-02-08 State Farm Mutual Automobile Insurance Company Autonomous vehicle action communications
US11119477B1 (en) 2016-01-22 2021-09-14 State Farm Mutual Automobile Insurance Company Anomalous condition detection and response for autonomous vehicles
US10395332B1 (en) 2016-01-22 2019-08-27 State Farm Mutual Automobile Insurance Company Coordinated autonomous vehicle automatic area scanning
US11719545B2 (en) 2016-01-22 2023-08-08 Hyundai Motor Company Autonomous vehicle component damage and salvage assessment
US11441916B1 (en) 2016-01-22 2022-09-13 State Farm Mutual Automobile Insurance Company Autonomous vehicle trip routing
US10134278B1 (en) 2016-01-22 2018-11-20 State Farm Mutual Automobile Insurance Company Autonomous vehicle application
US10297092B2 (en) * 2016-01-22 2019-05-21 Ford Global Technologies, Llc System and method for vehicular dynamic display
US10269075B2 (en) 2016-02-02 2019-04-23 Allstate Insurance Company Subjective route risk mapping and mitigation
JP6211113B2 (en) * 2016-02-03 2017-10-11 三菱電機株式会社 Vehicle approach detection device
EP3417240A4 (en) 2016-02-15 2019-07-17 Allstate Insurance Company Early notification of non-autonomous area
EP3417242B1 (en) 2016-02-15 2022-12-21 Allstate Insurance Company Real time risk assessment and operational changes with semi-autonomous vehicles
US10909629B1 (en) * 2016-02-15 2021-02-02 Allstate Insurance Company Testing autonomous cars
US10001988B2 (en) * 2016-02-18 2018-06-19 Toyota Jidosha Kabushiki Kaisha Compatibility module to support an automotive system upgrade
US9940832B2 (en) 2016-03-22 2018-04-10 Toyota Jidosha Kabushiki Kaisha Traffic management based on basic safety message data
US10346564B2 (en) * 2016-03-30 2019-07-09 Toyota Jidosha Kabushiki Kaisha Dynamic virtual object generation for testing autonomous vehicles in simulated driving scenarios
CN107292394A (en) * 2016-04-11 2017-10-24 富泰华工业(深圳)有限公司 Vehicle damage pricing system and method
US10152836B2 (en) 2016-04-19 2018-12-11 Mitchell International, Inc. Systems and methods for use of diagnostic scan tool in automotive collision repair
US9886841B1 (en) 2016-04-27 2018-02-06 State Farm Mutual Automobile Insurance Company Systems and methods for reconstruction of a vehicular crash
KR101661553B1 (en) * 2016-04-28 2016-10-04 주식회사 태원 Vehicle accident management system and operating method thereof
US10552914B2 (en) 2016-05-05 2020-02-04 Sensormatic Electronics, LLC Method and apparatus for evaluating risk based on sensor monitoring
US10956982B1 (en) 2016-05-11 2021-03-23 State Farm Mutual Automobile Insurance Company Systems and methods for allocating vehicle costs between vehicle users for anticipated trips
JP2017204208A (en) * 2016-05-13 2017-11-16 富士ゼロックス株式会社 Operation model construction system and operation model construction program
US10474168B1 (en) * 2016-05-16 2019-11-12 United Services Automobile Association Unmanned vehicle security guard
US10810676B2 (en) 2016-06-06 2020-10-20 Sensormatic Electronics, LLC Method and apparatus for increasing the density of data surrounding an event
US20170369086A1 (en) * 2016-06-22 2017-12-28 Xorail, LLC System, Method, and Apparatus for Testing a Train Management System on a Road-Rail Vehicle
JP6778872B2 (en) * 2016-06-28 2020-11-04 パナソニックIpマネジメント株式会社 Driving support device and driving support method
US10104496B2 (en) * 2016-07-01 2018-10-16 Laird Technologies, Inc. Telematics devices and systems
US10832331B1 (en) * 2016-07-11 2020-11-10 State Farm Mutual Automobile Insurance Company Systems and methods for allocating fault to autonomous vehicles
US11042938B1 (en) * 2016-08-08 2021-06-22 Allstate Insurance Company Driver identity detection and alerts
US10179586B2 (en) * 2016-08-11 2019-01-15 Toyota Motor Engineering & Manufacturing North America, Inc. Using information obtained from fleet of vehicles for informational display and control of an autonomous vehicle
US20180043903A1 (en) * 2016-08-15 2018-02-15 GM Global Technology Operations LLC Wirelessly communicating user-controlled vehicle preference settings with a remote location
US10571908B2 (en) * 2016-08-15 2020-02-25 Ford Global Technologies, Llc Autonomous vehicle failure mode management
US10759424B2 (en) * 2016-08-16 2020-09-01 Honda Motor Co., Ltd. Vehicle data selection system for modifying automated driving functionalities and method thereof
US10543852B2 (en) * 2016-08-20 2020-01-28 Toyota Motor Engineering & Manufacturing North America, Inc. Environmental driver comfort feedback for autonomous vehicle
JP6690056B2 (en) 2016-08-22 2020-04-28 ぺロトン テクノロジー インコーポレイテッド Control system architecture for motor vehicle
US10369998B2 (en) 2016-08-22 2019-08-06 Peloton Technology, Inc. Dynamic gap control for automated driving
JP6432572B2 (en) * 2016-08-22 2018-12-05 トヨタ自動車株式会社 Display device, display system
JP6701030B2 (en) * 2016-08-25 2020-05-27 クラリオン株式会社 In-vehicle device, log collection system
JP6853494B2 (en) * 2016-09-05 2021-03-31 日本電気株式会社 Drive recorder
JP6817531B2 (en) * 2016-09-05 2021-01-20 日本電気株式会社 Operation status recording device
WO2018049416A1 (en) 2016-09-12 2018-03-15 Zendrive, Inc. Method for mobile device-based cooperative data capture
US10093322B2 (en) 2016-09-15 2018-10-09 International Business Machines Corporation Automatically providing explanations for actions taken by a self-driving vehicle
US9919648B1 (en) * 2016-09-27 2018-03-20 Robert D. Pedersen Motor vehicle artificial intelligence expert system dangerous driving warning and control system and method
US10606276B2 (en) * 2016-09-30 2020-03-31 Faraday & Future Inc. User data-based autonomous vehicle system
US10131363B2 (en) 2016-10-24 2018-11-20 Ford Global Technologies, Llc Vehicle with mode guidance
US11037177B1 (en) 2016-10-28 2021-06-15 State Farm Mutual Automobile Insurance Company Vehicle component identification using driver profiles
US9823657B1 (en) * 2016-11-02 2017-11-21 Smartdrive Systems, Inc. Measuring operator readiness and readiness testing triggering in an autonomous vehicle
FR3058214B1 (en) * 2016-11-02 2020-06-12 Safran Electronics & Defense METHOD FOR DEVELOPING A SELF-NAVIGATION MAP FOR A VEHICLE
US9663118B1 (en) * 2016-11-02 2017-05-30 Smartdrive Systems, Inc. Autonomous vehicle operator performance tracking
US9963106B1 (en) * 2016-11-07 2018-05-08 Nio Usa, Inc. Method and system for authentication in autonomous vehicles
US10671514B2 (en) * 2016-11-15 2020-06-02 Inrix, Inc. Vehicle application simulation environment
US20210312558A1 (en) 2016-11-23 2021-10-07 State Farm Mutual Automobile Insurance Company Systems and methods for maintaining a distributed ledger of transactions pertaining to one or more smart contracts
WO2018098658A1 (en) * 2016-11-30 2018-06-07 深圳市大疆创新科技有限公司 Object testing method, device, and system
US10012993B1 (en) * 2016-12-09 2018-07-03 Zendrive, Inc. Method and system for risk modeling in autonomous vehicles
US11138490B2 (en) 2016-12-14 2021-10-05 Ajay Khoche Hierarchical combination of distributed statistics in a monitoring network
US11003978B2 (en) 2016-12-14 2021-05-11 Ajay Khoche Programmable network node roles in hierarchical communications network
EP4220603A1 (en) 2016-12-23 2023-08-02 Mobileye Vision Technologies Ltd. Navigational system with imposed constraints
US10252717B2 (en) 2017-01-10 2019-04-09 Toyota Jidosha Kabushiki Kaisha Vehicular mitigation system based on wireless vehicle data
US10322727B1 (en) * 2017-01-18 2019-06-18 State Farm Mutual Automobile Insurance Company Technology for assessing emotional state of vehicle operator
JP6800028B2 (en) * 2017-01-20 2020-12-16 株式会社クボタ Self-driving work vehicle
US9934625B1 (en) * 2017-01-31 2018-04-03 Uber Technologies, Inc. Detecting vehicle collisions based on moble computing device data
WO2018144917A1 (en) * 2017-02-02 2018-08-09 Cyber Physical Systems, Inc. Accident-severity scoring device, method, and system
US11521271B2 (en) * 2017-02-06 2022-12-06 Allstate Insurance Company Autonomous vehicle control systems with collision detection and response capabilities
US10341847B2 (en) * 2017-02-10 2019-07-02 International Business Machines Corporation Reactionary data transfer to cold storage
CA3052953C (en) 2017-02-10 2021-11-09 Nissan North America, Inc. Autonomous vehicle operational management blocking monitoring
JP6890757B2 (en) 2017-02-10 2021-06-18 ニッサン ノース アメリカ,インク Partially Observed Markov Decision Process Autonomous vehicle motion management including operating a model instance
DE102017202415A1 (en) * 2017-02-15 2018-08-16 Bayerische Motoren Werke Aktiengesellschaft Collision avoidance with cross traffic
US10118628B2 (en) 2017-02-21 2018-11-06 Allstate Insurance Company Data processing system for guidance, control, and testing autonomous vehicle features and driver response
US10712163B2 (en) 2017-02-23 2020-07-14 International Business Machines Corporation Vehicle routing and notifications based on characteristics
US10142137B2 (en) 2017-03-02 2018-11-27 Micron Technology, Inc. Wireless devices and systems including examples of full duplex transmission
JP7069456B2 (en) 2017-03-17 2022-05-18 ザ・リージェンツ・オブ・ザ・ユニバーシティ・オブ・ミシガン Methods and equipment for building useful results to guide multi-policy decision making
US10037683B1 (en) * 2017-03-24 2018-07-31 GM Global Technology Operations LLC Crash detection using GNSS velocity measurements and bus monitoring
US10733311B2 (en) * 2017-03-29 2020-08-04 International Business Machines Corporation Cognitive internet of things (IoT) gateways for data security and privacy protection in real-time context-based data applications
US20180285977A1 (en) * 2017-03-29 2018-10-04 The Travelers Indemnity Company Systems and methods for multi-party sensors
JP6593712B2 (en) * 2017-03-30 2019-10-23 マツダ株式会社 Vehicle driving support system
US10846947B2 (en) * 2017-03-31 2020-11-24 Honeywell International Inc. System and method for analyzing vehicle systems during vehicle travel
US20180286246A1 (en) * 2017-03-31 2018-10-04 Intel Corporation Sensor-derived road hazard detection and reporting
JP6539363B2 (en) 2017-04-07 2019-07-03 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカPanasonic Intellectual Property Corporation of America Illegal communication detection method, illegal communication detection system and program
WO2018186053A1 (en) 2017-04-07 2018-10-11 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ Method for detecting unauthorized communication, system for detecting unauthorized communication, and program
US10290214B2 (en) * 2017-04-11 2019-05-14 Denso International America, Inc. Lane change system and lane change controller
US11214143B2 (en) * 2017-05-02 2022-01-04 Motional Ad Llc Visually obstructed object detection for automated vehicle using V2V/V2I communications
US10942525B2 (en) 2017-05-09 2021-03-09 Uatc, Llc Navigational constraints for autonomous vehicles
JP6638695B2 (en) * 2017-05-18 2020-01-29 トヨタ自動車株式会社 Autonomous driving system
US10789835B2 (en) 2017-05-23 2020-09-29 Uatc, Llc Fractional risk performance evaluation for autonomous vehicles
US10262471B2 (en) 2017-05-23 2019-04-16 Uber Technologies, Inc. Autonomous vehicle degradation level monitoring
US10501091B2 (en) 2017-05-23 2019-12-10 Uber Technologies, Inc. Software version and mode switching for autonomous vehicles
US11282016B2 (en) 2017-05-23 2022-03-22 Uatc, Llc Individualized risk vehicle matching for an on-demand transportation service
US10762447B2 (en) * 2017-05-23 2020-09-01 Uatc, Llc Vehicle selection for on-demand transportation services
US10489721B2 (en) 2017-05-23 2019-11-26 Uatc, Llc Path segment risk regression system for on-demand transportation services
US10697789B2 (en) 2017-05-23 2020-06-30 Uatc, Llc Individualized risk routing for human drivers
US11288612B2 (en) 2017-05-23 2022-03-29 Uatc, Llc Generalized risk routing for human drivers
US11080806B2 (en) 2017-05-23 2021-08-03 Uber Technologies, Inc. Non-trip risk matching and routing for on-demand transportation services
US10884902B2 (en) * 2017-05-23 2021-01-05 Uatc, Llc Software version verification for autonomous vehicles
US11282009B2 (en) 2017-05-23 2022-03-22 Uatc, Llc Fleet utilization efficiency for on-demand transportation services
KR101964919B1 (en) * 2017-05-26 2019-08-13 주식회사 만도 Method and Apparatus for controlling parking of vehicle
US10449916B2 (en) * 2017-06-05 2019-10-22 Paccar Inc. Method for improving fuel economy by influencing driver behavior
CN109032102B (en) * 2017-06-09 2020-12-18 百度在线网络技术(北京)有限公司 Unmanned vehicle testing method, device, equipment and storage medium
US11036221B1 (en) * 2017-06-12 2021-06-15 United Services Automobile Association (Usaa) Systems and methods for autonomous vehicle risk management
KR20220158873A (en) * 2017-06-16 2022-12-01 모셔널 에이디 엘엘씨 Intervention in operation of a vehicle having autonomous driving capabilities
US10740988B2 (en) * 2017-06-16 2020-08-11 nuTonomy Inc. Intervention in operation of a vehicle having autonomous driving capabilities
US11112789B2 (en) * 2017-06-16 2021-09-07 Motional Ad Llc Intervention in operation of a vehicle having autonomous driving capabilities
US10599141B2 (en) * 2017-06-16 2020-03-24 nuTonomy Inc. Intervention in operation of a vehicle having autonomous driving capabilities
US10627810B2 (en) * 2017-06-16 2020-04-21 nuTonomy Inc. Intervention in operation of a vehicle having autonomous driving capabilities
US10514692B2 (en) * 2017-06-16 2019-12-24 nuTonomy Inc. Intervention in operation of a vehicle having autonomous driving capabilities
US10317899B2 (en) 2017-06-16 2019-06-11 nuTonomy Inc. Intervention in operation of a vehicle having autonomous driving capabilities
DE102017210961A1 (en) * 2017-06-28 2019-01-03 Audi Ag Method for the at least partially automated operation of a motor vehicle
US10304329B2 (en) 2017-06-28 2019-05-28 Zendrive, Inc. Method and system for determining traffic-related characteristics
US10730526B2 (en) * 2017-07-14 2020-08-04 Ccc Information Services Inc. Driver assist design analysis system
US11267481B2 (en) * 2017-07-14 2022-03-08 Ccc Intelligent Solutions Inc. Driver assist design analysis system
JP6841329B2 (en) * 2017-07-20 2021-03-17 日産自動車株式会社 Vehicle travel control method and vehicle travel control device
DE102017213073B4 (en) * 2017-07-28 2019-06-19 Ford Global Technologies, Llc Method for determining a stop location of a motor vehicle
US10558224B1 (en) * 2017-08-10 2020-02-11 Zoox, Inc. Shared vehicle obstacle data
US11430071B2 (en) 2017-08-16 2022-08-30 Mobileye Vision Technologies Ltd. Navigation based on liability constraints
US10431023B1 (en) * 2017-08-21 2019-10-01 Uber Technologies, Inc. Systems and methods to test an autonomous vehicle
US10635101B2 (en) * 2017-08-21 2020-04-28 Honda Motor Co., Ltd. Methods and systems for preventing an autonomous vehicle from transitioning from an autonomous driving mode to a manual driving mode based on a risk model
US11107300B2 (en) * 2017-08-23 2021-08-31 Panasonic Intellectual Property Corporation Of America Driving management system, vehicle, and information processing method
EP3447993B1 (en) * 2017-08-23 2021-09-29 Panasonic Intellectual Property Corporation of America Driving management system, vehicle, and information processing method
US11362882B2 (en) * 2017-08-25 2022-06-14 Veniam, Inc. Methods and systems for optimal and adaptive urban scanning using self-organized fleets of autonomous vehicles
DE112017007793T5 (en) * 2017-08-25 2020-05-28 Ford Global Technologies, Llc ANOMALY DETECTION IN THE INTERIOR OF AN AUTONOMOUS VEHICLE
US20220067838A1 (en) * 2017-09-06 2022-03-03 State Farm Mutual Automobile Insurance Company Technology for Analyzing Previous Vehicle Usage to Identify Customer Opportunities
US10794710B1 (en) 2017-09-08 2020-10-06 Perceptin Shenzhen Limited High-precision multi-layer visual and semantic map by autonomous units
US10554375B2 (en) 2017-09-11 2020-02-04 Micron Technology, Inc. Full duplex device-to-device cooperative communication
CN107742417B (en) * 2017-09-13 2019-12-17 成都路行通信息技术有限公司 Vehicle accident alarm method and device
US20190077353A1 (en) * 2017-09-13 2019-03-14 International Business Machines Corporation Cognitive-based vehicular incident assistance
US10984481B1 (en) 2017-09-15 2021-04-20 United Services Automobile Association (Usaa) Systems and methods for determining premium rate for semi-autonomous and/or autonomous vehicles
US11037248B1 (en) 2017-10-11 2021-06-15 State Farm Mutual Automobile Insurance Company Cost sharing based upon in-car audio
DE102017218222A1 (en) * 2017-10-12 2019-04-18 Continental Automotive Gmbh Determining the position of a later breakpoint of a vehicle
US10445817B2 (en) 2017-10-16 2019-10-15 Allstate Insurance Company Geotagging location data
DE102017218703A1 (en) * 2017-10-19 2019-04-25 Continental Teves Ag & Co. Ohg Method for determining the value of parameters
EP3698340A4 (en) 2017-10-20 2021-07-14 Zendrive, Inc. Method and system for vehicular-related communications
US10611381B2 (en) 2017-10-24 2020-04-07 Ford Global Technologies, Llc Decentralized minimum risk condition vehicle control
WO2019088977A1 (en) * 2017-10-30 2019-05-09 Nissan North America, Inc. Continual planning and metareasoning for controlling an autonomous vehicle
WO2019089015A1 (en) 2017-10-31 2019-05-09 Nissan North America, Inc. Autonomous vehicle operation with explicit occlusion reasoning
US11027751B2 (en) 2017-10-31 2021-06-08 Nissan North America, Inc. Reinforcement and model learning for vehicle operation
US10802486B1 (en) * 2017-11-01 2020-10-13 United Services Automobile Association (Usaa) Autonomous vehicle repair
JP6977490B2 (en) * 2017-11-06 2021-12-08 トヨタ自動車株式会社 Information processing equipment, information processing system and information processing method
GB201719108D0 (en) * 2017-11-17 2018-01-03 Xtract360 Ltd Collision evaluation
JP7052312B2 (en) * 2017-11-20 2022-04-12 トヨタ自動車株式会社 Driving support device
US10629008B2 (en) * 2017-11-20 2020-04-21 Ford Global Technologies, Llc Vehicle diagnostic operation
CN107742208A (en) * 2017-11-23 2018-02-27 中国平安财产保险股份有限公司 Vehicle is in danger querying method, device, equipment and the computer media of flow
US10278039B1 (en) 2017-11-27 2019-04-30 Zendrive, Inc. System and method for vehicle sensing and analysis
US20190164007A1 (en) * 2017-11-30 2019-05-30 TuSimple Human driving behavior modeling system using machine learning
EP3717324A4 (en) 2017-11-30 2021-06-02 Nissan North America, Inc. Autonomous vehicle operational management scenarios
US11040726B2 (en) * 2017-12-15 2021-06-22 Baidu Usa Llc Alarm system of autonomous driving vehicles (ADVs)
US20190188635A1 (en) * 2017-12-15 2019-06-20 Walmart Apollo, Llc Automated vehicle and method for servicing disabled vehicles
DE102017223005A1 (en) * 2017-12-18 2019-06-19 Robert Bosch Gmbh Method and device for providing injury information about an injury to an unprotected road user in a collision with a vehicle
SE542387C2 (en) * 2017-12-20 2020-04-21 Scania Cv Ab Method and control arrangement in a transportation surveillance system, monitoring a system comprising autonomous vehicles, for assisting a human operator in predictive decision making
US10325423B1 (en) * 2017-12-20 2019-06-18 ANI Technologies Private Limited Method and system for validating states of components of vehicle
CN107977896A (en) * 2017-12-21 2018-05-01 江西爱驰亿维实业有限公司 The accounting method and device that car insurance is taken
US10877999B2 (en) * 2017-12-21 2020-12-29 Micron Technology, Inc. Programmatically identifying a personality of an autonomous vehicle
WO2020204871A1 (en) 2017-12-22 2020-10-08 Nissan North America, Inc. Shared autonomous vehicle operational management
US11247702B2 (en) * 2017-12-25 2022-02-15 Hitachi Astemo, Ltd. Vehicle control device and electronic control system
US11104331B2 (en) * 2017-12-28 2021-08-31 Intel Corporation Autonomous techniques for vehicles with balance input
WO2019132930A1 (en) * 2017-12-28 2019-07-04 Intel Corporation System and method for simulation of autonomous vehicles
US11157002B2 (en) * 2017-12-28 2021-10-26 Intel Corporation Methods, systems, articles of manufacture and apparatus to improve autonomous machine capabilities
CN110226317B (en) * 2018-01-03 2022-03-08 百度(美国)有限责任公司 Data authentication method, device and system
US11009359B2 (en) 2018-01-05 2021-05-18 Lacuna Technologies Inc. Transportation systems and related methods
US10875503B2 (en) 2018-01-06 2020-12-29 Toyota Motor Engineering & Manufacturing North America, Inc. System and method for anti-theft control for autonomous vehicle
US10831636B2 (en) 2018-01-08 2020-11-10 Waymo Llc Software validation for autonomous vehicles
US20190213684A1 (en) * 2018-01-10 2019-07-11 Accenture Global Solutions Limited Integrated vehicular monitoring and communication system
US11206050B2 (en) 2018-02-06 2021-12-21 Micron Technology, Inc. Self interference noise cancellation to support multiple frequency bands
US11527112B2 (en) * 2018-02-15 2022-12-13 ANI Technologies Private Limited Vehicle allocation method and system
US11087571B2 (en) * 2018-02-16 2021-08-10 General Motors Llc Monitoring quality of care at vehicle
US10726645B2 (en) 2018-02-16 2020-07-28 Ford Global Technologies, Llc Vehicle diagnostic operation
US11110941B2 (en) 2018-02-26 2021-09-07 Renault S.A.S. Centralized shared autonomous vehicle operational management
US11108804B2 (en) * 2018-02-27 2021-08-31 Blackberry Limited Providing secure inter-vehicle data communications
US10460577B2 (en) 2018-02-28 2019-10-29 Pony Ai Inc. Directed alert notification by autonomous-driving vehicle
US11269326B2 (en) * 2018-03-07 2022-03-08 Mile Auto, Inc. Monitoring and tracking mode of operation of vehicles to determine services
US11086318B1 (en) * 2018-03-21 2021-08-10 Uatc, Llc Systems and methods for a scenario tagger for autonomous vehicles
JP7013993B2 (en) * 2018-03-26 2022-02-01 トヨタ自動車株式会社 Diagnostic device and diagnostic method
JP2019169083A (en) * 2018-03-26 2019-10-03 株式会社デンソー Determination device and method
US11206375B2 (en) 2018-03-28 2021-12-21 Gal Zuckerman Analyzing past events by utilizing imagery data captured by a plurality of on-road vehicles
CN110320898B (en) * 2018-03-28 2023-09-29 博泰车联网科技(上海)股份有限公司 Vehicle traffic accident remote taking over rescue method and system based on 5G Internet of vehicles
US10762363B2 (en) * 2018-03-30 2020-09-01 Toyota Jidosha Kabushiki Kaisha Road sign recognition for connected vehicles
US20190300017A1 (en) * 2018-04-02 2019-10-03 GM Global Technology Operations LLC Method of controlling a vehicle
US10915159B2 (en) * 2018-04-03 2021-02-09 GM Global Technology Operations LLC Method of controlling a vehicle to adjust perception system energy usage
US10971155B2 (en) * 2018-04-12 2021-04-06 Honeywell International Inc. Aircraft systems and methods for monitoring onboard communications
US10331128B1 (en) * 2018-04-20 2019-06-25 Lyft, Inc. Control redundancy
US10698753B2 (en) * 2018-04-20 2020-06-30 Ratheon Company Mitigating device vulnerabilities in software
CN108932577A (en) * 2018-04-25 2018-12-04 广州广电研究院有限公司 A kind of assessment of business risk and early warning system
CN108389418A (en) * 2018-04-27 2018-08-10 北京智行者科技有限公司 The dispatching method of automatic driving vehicle
US11164016B2 (en) * 2018-05-17 2021-11-02 Uatc, Llc Object detection and property determination for autonomous vehicles
US10493938B1 (en) * 2018-05-22 2019-12-03 Bank Of America Corporation Real-time vehicle environment recognition and collision identification system
US11507928B2 (en) * 2018-06-05 2022-11-22 International Business Machines Corporation Blockchain and cryptocurrency for real-time vehicle accident management
CN108876137B (en) * 2018-06-11 2021-05-28 中国标准化研究院 Automobile safety risk early warning method and system based on multi-source information
US11287816B2 (en) 2018-06-11 2022-03-29 Uatc, Llc Navigational constraints for autonomous vehicles
US20190384292A1 (en) * 2018-06-15 2019-12-19 Allstate Insurance Company Processing System For Evaluating Autonomous Vehicle Control Systems Through Continuous Learning
KR102060303B1 (en) * 2018-06-20 2019-12-30 현대모비스 주식회사 Apparatus for controlling autonomous driving and method thereof
CN108845509A (en) * 2018-06-27 2018-11-20 中汽研(天津)汽车工程研究院有限公司 A kind of adaptive learning algorithms algorithm development system and method
US10769869B2 (en) 2018-06-27 2020-09-08 International Business Machines Corporation Self-driving vehicle integrity management on a blockchain
US11120688B2 (en) 2018-06-29 2021-09-14 Nissan North America, Inc. Orientation-adjust actions for autonomous vehicle operational management
WO2020018688A1 (en) 2018-07-20 2020-01-23 May Mobility, Inc. A multi-perspective system and method for behavioral policy selection by an autonomous agent
US10909866B2 (en) * 2018-07-20 2021-02-02 Cybernet Systems Corp. Autonomous transportation system and methods
US10614709B2 (en) 2018-07-24 2020-04-07 May Mobility, Inc. Systems and methods for implementing multimodal safety operations with an autonomous agent
US11295560B2 (en) * 2018-08-01 2022-04-05 Ford Global Technologies, Llc Cloud-managed validation and execution for diagnostic requests
US11138418B2 (en) 2018-08-06 2021-10-05 Gal Zuckerman Systems and methods for tracking persons by utilizing imagery data captured by on-road vehicles
US20200055524A1 (en) * 2018-08-20 2020-02-20 Alberto LACAZE System and method for verifying that a self-driving vehicle follows traffic ordinances
US10831207B1 (en) * 2018-08-22 2020-11-10 BlueOwl, LLC System and method for evaluating the performance of a vehicle operated by a driving automation system
CN109146303A (en) * 2018-08-30 2019-01-04 百度在线网络技术(北京)有限公司 Car operation control method, device and equipment
DE102018215329A1 (en) 2018-08-31 2020-03-05 Robert Bosch Gmbh Computer-implemented simulation method and arrangement for testing control units
CN109374310A (en) * 2018-09-07 2019-02-22 百度在线网络技术(北京)有限公司 Automatic driving vehicle test method, device and storage medium
US10922970B2 (en) * 2018-09-14 2021-02-16 ANI Technologies Private Ltd. Methods and systems for facilitating driving-assistance to drivers of vehicles
US10650623B2 (en) * 2018-09-18 2020-05-12 Avinew, Inc. Detecting of automatic driving
US11495028B2 (en) * 2018-09-28 2022-11-08 Intel Corporation Obstacle analyzer, vehicle control system, and methods thereof
US10995462B2 (en) 2018-10-10 2021-05-04 International Business Machines Corporation Autonomous mobile attenuator system
CN109447443A (en) * 2018-10-18 2019-03-08 阳光人寿保险股份有限公司 Index calculating method and device
US10896116B1 (en) * 2018-10-19 2021-01-19 Waymo Llc Detecting performance regressions in software for controlling autonomous vehicles
US11145000B1 (en) * 2018-10-31 2021-10-12 United Services Automobile Association (Usaa) Method and system for detecting use of vehicle safety systems
US11288750B1 (en) * 2018-10-31 2022-03-29 United Services Automobile Association (Usaa) Method and system for automatically detecting vehicle collisions for insurance claims
CN111160677A (en) * 2018-11-08 2020-05-15 中国石油化工股份有限公司 Accident scenario construction method and system for accident scenario construction
US10885280B2 (en) 2018-11-14 2021-01-05 International Business Machines Corporation Event detection with conversation
US11378965B2 (en) * 2018-11-15 2022-07-05 Toyota Research Institute, Inc. Systems and methods for controlling a vehicle based on determined complexity of contextual environment
US11587366B1 (en) 2018-11-20 2023-02-21 State Farm Mutual Automobile Insurance Company Systems and methods for selecting locations to validate automated vehicle data transmission
US11593539B2 (en) * 2018-11-30 2023-02-28 BlueOwl, LLC Systems and methods for facilitating virtual vehicle operation based on real-world vehicle operation data
US10633003B1 (en) 2018-12-05 2020-04-28 Here Global B.V. Method, apparatus, and computer readable medium for verifying a safe vehicle operation via a portable device
KR102166532B1 (en) * 2018-12-11 2020-10-16 주식회사 경신 Apparatus and method for selecting a risk rating of an autonomous vehicle load
US11340094B2 (en) * 2018-12-12 2022-05-24 Baidu Usa Llc Updating map data for autonomous driving vehicles based on sensor data
US11783423B1 (en) 2018-12-14 2023-10-10 Allstate Insurance Company Connected home system with risk units
CN109788030B (en) * 2018-12-17 2021-08-03 北京百度网讯科技有限公司 Unmanned vehicle data processing method, device and system and storage medium
US11320819B2 (en) * 2018-12-17 2022-05-03 Here Global B.V. Method, apparatus and computer program product for estimating accuracy of local hazard warnings
WO2020132305A1 (en) * 2018-12-19 2020-06-25 Zoox, Inc. Safe system operation using latency determinations and cpu usage determinations
KR102636741B1 (en) * 2018-12-24 2024-02-16 현대자동차주식회사 Automatic Driving control apparatus, vehicle having the same and method for controlling the same
US11741763B2 (en) * 2018-12-26 2023-08-29 Allstate Insurance Company Systems and methods for system generated damage analysis
US10977738B2 (en) * 2018-12-27 2021-04-13 Futurity Group, Inc. Systems, methods, and platforms for automated quality management and identification of errors, omissions and/or deviations in coordinating services and/or payments responsive to requests for coverage under a policy
US11214268B2 (en) * 2018-12-28 2022-01-04 Intel Corporation Methods and apparatus for unsupervised multimodal anomaly detection for autonomous vehicles
US10928827B2 (en) 2019-01-07 2021-02-23 Toyota Research Institute, Inc. Systems and methods for generating a path for a vehicle
US11410243B2 (en) * 2019-01-08 2022-08-09 Clover Health Segmented actuarial modeling
US11100793B2 (en) * 2019-01-15 2021-08-24 Waycare Technologies Ltd. System and method for detection and quantification of irregular traffic congestion
CN109808697B (en) * 2019-01-16 2021-09-07 北京百度网讯科技有限公司 Vehicle control method, device and equipment
US11257108B2 (en) * 2019-01-18 2022-02-22 Samuel Salloum Systems and methods for dynamic product offerings
US20200241542A1 (en) * 2019-01-25 2020-07-30 Bayerische Motoren Werke Aktiengesellschaft Vehicle Equipped with Accelerated Actor-Critic Reinforcement Learning and Method for Accelerating Actor-Critic Reinforcement Learning
US11360442B2 (en) * 2019-01-31 2022-06-14 Morgan Stanley Services Group Inc. Exposure minimization response by artificial intelligence
US11810363B2 (en) * 2019-01-31 2023-11-07 Toyota Motor North America, Inc. Systems and methods for image processing using mobile devices
US11012809B2 (en) 2019-02-08 2021-05-18 Uber Technologies, Inc. Proximity alert system
US11119492B2 (en) * 2019-02-12 2021-09-14 Sf Motors, Inc. Automatically responding to emergency service vehicles by an autonomous vehicle
US10969470B2 (en) 2019-02-15 2021-04-06 May Mobility, Inc. Systems and methods for intelligently calibrating infrastructure devices using onboard sensors of an autonomous agent
JP7163814B2 (en) * 2019-02-18 2022-11-01 トヨタ自動車株式会社 vehicle system
US10885725B2 (en) 2019-03-18 2021-01-05 International Business Machines Corporation Identifying a driving mode of an autonomous vehicle
EP3942253A4 (en) * 2019-03-18 2023-04-19 Cognata Ltd. Systems and methods for evaluation of vehicle technologies
US10913428B2 (en) 2019-03-18 2021-02-09 Pony Ai Inc. Vehicle usage monitoring
US11710097B2 (en) 2019-03-22 2023-07-25 BlueOwl, LLC Systems and methods for obtaining incident information to reduce fraud
US11570625B2 (en) * 2019-03-25 2023-01-31 Micron Technology, Inc. Secure vehicle communications architecture for improved blind spot and driving distance detection
US11087569B2 (en) * 2019-03-25 2021-08-10 International Business Machines Corporation Vehicle accident data management system
US11122062B2 (en) 2019-03-26 2021-09-14 International Business Machines Corporation Remote interference assessment and response for autonomous vehicles
US10726642B1 (en) 2019-03-29 2020-07-28 Toyota Motor North America, Inc. Vehicle data sharing with interested parties
US10535207B1 (en) 2019-03-29 2020-01-14 Toyota Motor North America, Inc. Vehicle data sharing with interested parties
US11048261B1 (en) 2019-04-05 2021-06-29 State Farm Mutual Automobile Insurance Company Systems and methods for evaluating autonomous vehicle software interactions for proposed trips
US11321972B1 (en) 2019-04-05 2022-05-03 State Farm Mutual Automobile Insurance Company Systems and methods for detecting software interactions for autonomous vehicles within changing environmental conditions
US11086996B2 (en) 2019-04-12 2021-08-10 International Business Machines Corporation Automatic idle-state scanning for malicious code
US11603110B2 (en) 2019-04-18 2023-03-14 Kyndryl, Inc. Addressing vehicle sensor abnormalities
EP3960576A1 (en) * 2019-04-24 2022-03-02 Walter Steven Rosenbaum Method and system for analysing the control of a vehicle
US10958737B2 (en) 2019-04-29 2021-03-23 Synamedia Limited Systems and methods for distributing content
US10807527B1 (en) 2019-05-01 2020-10-20 Smartdrive Systems, Inc. Systems and methods for verifying whether vehicle operators are paying attention
US10887724B2 (en) * 2019-05-01 2021-01-05 International Business Machines Corporation Locating a mobile device and notifying a user of the mobile device location
US11609579B2 (en) * 2019-05-01 2023-03-21 Smartdrive Systems, Inc. Systems and methods for using risk profiles based on previously detected vehicle events to quantify performance of vehicle operators
US11300977B2 (en) 2019-05-01 2022-04-12 Smartdrive Systems, Inc. Systems and methods for creating and using risk profiles for fleet management of a fleet of vehicles
US11262763B2 (en) 2019-05-01 2022-03-01 Smartdrive Systems, Inc. Systems and methods for using risk profiles for creating and deploying new vehicle event definitions to a fleet of vehicles
US11491999B2 (en) 2019-05-08 2022-11-08 Toyota Research Institute, Inc. Adjusting an operating mode of a vehicle based on an expected resource level
US11247695B2 (en) 2019-05-14 2022-02-15 Kyndryl, Inc. Autonomous vehicle detection
DE102019207092A1 (en) * 2019-05-16 2020-11-19 Zf Friedrichshafen Ag Accident data recorder for driver assistance systems
CN111144606B (en) * 2019-05-17 2020-09-15 深圳市德塔防爆电动汽车有限公司 Safety failure risk prediction method for electric vehicle and electric vehicle
US11227490B2 (en) 2019-06-18 2022-01-18 Toyota Motor North America, Inc. Identifying changes in the condition of a transport
CN114041176A (en) * 2019-07-01 2022-02-11 索尼集团公司 Security performance evaluation device, security performance evaluation method, information processing device, and information processing method
US11565711B2 (en) 2019-07-08 2023-01-31 Morgan State University System and method for generating vehicle speed alerts
US11163270B2 (en) 2019-07-10 2021-11-02 Lear Corporation Vehicle occupant data collection and processing with artificial intelligence
US10875537B1 (en) 2019-07-12 2020-12-29 Toyota Research Institute, Inc. Systems and methods for monitoring the situational awareness of a vehicle according to reactions of a vehicle occupant
US11423775B2 (en) * 2019-07-18 2022-08-23 International Business Machines Corporation Predictive route congestion management
US11391257B2 (en) * 2019-07-23 2022-07-19 Ford Global Technologies, Llc Power supply during vehicle startup
US11529886B2 (en) 2019-07-23 2022-12-20 Ford Global Technologies, Llc Power supply during vehicle off state
US20210024058A1 (en) * 2019-07-25 2021-01-28 Cambridge Mobile Telematics Inc. Evaluating the safety performance of vehicles
CN112309152B (en) * 2019-08-01 2022-11-18 华为技术有限公司 Vehicle-road cooperation method and device
JP2021026596A (en) * 2019-08-07 2021-02-22 トヨタ自動車株式会社 Driving behavior evaluation device, driving behavior evaluation method, and driving behavior evaluation program
US11587130B1 (en) 2019-08-28 2023-02-21 State Farm Mutual Automobile Insurance Company Systems and methods for generating user offerings responsive to telematics data
KR20210026248A (en) * 2019-08-29 2021-03-10 현대자동차주식회사 Apparatus for notifying accident of a vehicle, system having the same and method thereof
US10979097B2 (en) 2019-09-05 2021-04-13 Micron Technology, Inc. Wireless devices and systems including examples of full duplex transmission using neural networks or recurrent neural networks
US10957189B1 (en) * 2019-09-09 2021-03-23 GM Global Technology Operations LLC Automatic vehicle alert and reporting systems and methods
US11024169B2 (en) * 2019-09-09 2021-06-01 International Business Machines Corporation Methods and systems for utilizing vehicles to investigate events
US11459028B2 (en) * 2019-09-12 2022-10-04 Kyndryl, Inc. Adjusting vehicle sensitivity
US11625624B2 (en) * 2019-09-24 2023-04-11 Ford Global Technologies, Llc Vehicle-to-everything (V2X)-based real-time vehicular incident risk prediction
US20210101591A1 (en) * 2019-10-02 2021-04-08 Navisaf S.A.S System and method for lowering risk during operation of a moving vehicle
CN114787889A (en) * 2019-10-04 2022-07-22 索尼集团公司 Information processing apparatus, information processing method, and information processing apparatus
US11741305B2 (en) 2019-10-07 2023-08-29 The Toronto-Dominion Bank Systems and methods for automatically assessing fault in relation to motor vehicle collisions
DE102019127974B4 (en) * 2019-10-16 2023-11-02 Allianz Partners SAS Method and system for evaluating driving behavior
US11609580B2 (en) 2019-10-17 2023-03-21 Tusimple, Inc. Workflow management system
WO2021079329A1 (en) * 2019-10-24 2021-04-29 50Fifty Luxury Car Insurance (Pty) Ltd Vehicle usage
US11288901B2 (en) 2019-10-24 2022-03-29 Ford Globl Technologies, Llc Vehicle impact detection
US11417208B1 (en) 2019-10-29 2022-08-16 BlueOwl, LLC Systems and methods for fraud prevention based on video analytics
FR3103219B1 (en) * 2019-11-19 2021-10-08 Vitesco Technologies Method for managing sporadic anomalies of a motor vehicle system
US11635758B2 (en) 2019-11-26 2023-04-25 Nissan North America, Inc. Risk aware executor with action set recommendations
US11899454B2 (en) 2019-11-26 2024-02-13 Nissan North America, Inc. Objective-based reasoning in autonomous vehicle decision-making
DE102019218614A1 (en) * 2019-11-29 2021-06-02 Volkswagen Aktiengesellschaft Module prioritization method, module prioritization module, motor vehicle
US11775010B2 (en) 2019-12-02 2023-10-03 Zendrive, Inc. System and method for assessing device usage
US11674820B2 (en) * 2019-12-02 2023-06-13 Chevron U.S.A. Inc. Road safety analytics dashboard and risk minimization routing system and method
US11175152B2 (en) 2019-12-03 2021-11-16 Zendrive, Inc. Method and system for risk determination of a route
CN111028530A (en) * 2019-12-06 2020-04-17 广东科学技术职业学院 Method and device for controlling unmanned equipment to move and unmanned equipment
US11402214B2 (en) * 2019-12-10 2022-08-02 Here Global B.V. Method and apparatus for providing aerial route calculation in a three-dimensional space
US11513520B2 (en) * 2019-12-10 2022-11-29 International Business Machines Corporation Formally safe symbolic reinforcement learning on visual inputs
WO2021126648A1 (en) * 2019-12-17 2021-06-24 Zoox, Inc. Fault coordination and management
US11180156B2 (en) 2019-12-17 2021-11-23 Zoox, Inc. Fault coordination and management
US11535270B2 (en) 2019-12-17 2022-12-27 Zoox, Inc. Fault coordination and management
US11551494B2 (en) 2019-12-23 2023-01-10 Uatc, Llc Predictive mobile test device control for autonomous vehicle testing
US11613269B2 (en) 2019-12-23 2023-03-28 Nissan North America, Inc. Learning safety and human-centered constraints in autonomous vehicles
US11300957B2 (en) 2019-12-26 2022-04-12 Nissan North America, Inc. Multiple objective explanation and control interface design
US10971005B1 (en) * 2019-12-26 2021-04-06 Continental Automotive Systems, Inc. Determining I2X traffic-participant criticality
US11765067B1 (en) 2019-12-28 2023-09-19 Waymo Llc Methods and apparatus for monitoring a sensor validator
US11599951B1 (en) * 2020-01-13 2023-03-07 State Farm Mutual Automobile Insurance Company Systems and methods for generating on-demand insurance policies
WO2021150498A1 (en) 2020-01-20 2021-07-29 BlueOwl, LLC Systems and methods for training and applying virtual occurrences and granting in-game resources to a virtual character using telematics data of one or more real trips
US11385656B2 (en) * 2020-01-22 2022-07-12 Huawei Technologies Co., Ltd. System, device and method of identifying and updating the operational design domain of an autonomous vehicle
US11714971B2 (en) 2020-01-31 2023-08-01 Nissan North America, Inc. Explainability of autonomous vehicle decision making
US11577746B2 (en) 2020-01-31 2023-02-14 Nissan North America, Inc. Explainability of autonomous vehicle decision making
US10834538B1 (en) 2020-02-12 2020-11-10 International Business Machines Corporation Locating a mobile device and notifying a user of the mobile device location
JP6937856B2 (en) * 2020-02-13 2021-09-22 本田技研工業株式会社 Driving assistance devices and vehicles
US11312386B2 (en) 2020-02-19 2022-04-26 Autotalks Ltd. System and method for driver risk grading based on vehicle-to everything (V2X) communication
US11461087B2 (en) 2020-02-28 2022-10-04 Toyota Motor North America, Inc. Transport sensor data update
US11514729B2 (en) 2020-02-28 2022-11-29 Toyota Motor North America, Inc. Transport behavior observation
US11782438B2 (en) 2020-03-17 2023-10-10 Nissan North America, Inc. Apparatus and method for post-processing a decision-making model of an autonomous vehicle using multivariate data
US11560108B2 (en) * 2020-03-19 2023-01-24 Zf Friedrichshafen Ag Vehicle safety system and method implementing weighted active-passive crash mode classification
WO2021195002A1 (en) 2020-03-21 2021-09-30 Trackonomy Systems, Inc. Wireless sensor nodes for equipment monitoring
JP2021149806A (en) * 2020-03-23 2021-09-27 本田技研工業株式会社 Notification device
JP7406432B2 (en) * 2020-03-31 2023-12-27 本田技研工業株式会社 Mobile object control device, mobile object control method, and program
JP7251513B2 (en) * 2020-04-08 2023-04-04 トヨタ自動車株式会社 Automatic valet parking system and service provision method
US11615200B2 (en) * 2020-04-14 2023-03-28 Toyota Motor North America, Inc. Providing video evidence
US11450099B2 (en) 2020-04-14 2022-09-20 Toyota Motor North America, Inc. Video accident reporting
US11508189B2 (en) 2020-04-14 2022-11-22 Toyota Motor North America, Inc. Processing of accident report
US11258473B2 (en) 2020-04-14 2022-02-22 Micron Technology, Inc. Self interference noise cancellation to support multiple frequency bands with neural networks or recurrent neural networks
US11710186B2 (en) 2020-04-24 2023-07-25 Allstate Insurance Company Determining geocoded region based rating systems for decisioning outputs
US11915319B1 (en) * 2020-04-28 2024-02-27 State Farm Mutual Automobile Insurance Company Dialogue advisor for claim loss reporting tool
US11080949B1 (en) * 2020-05-04 2021-08-03 Timothy Just Predictive vehicle operating assistance
US11847919B2 (en) * 2020-05-19 2023-12-19 Toyota Motor North America, Inc. Control of transport en route
US11376502B2 (en) 2020-05-28 2022-07-05 Microsoft Technology Licensing, Llc Adjudicating fault in a virtual simulation environment
USD960129S1 (en) 2020-06-09 2022-08-09 Geotab Inc. Case for electronic communication device
US11610504B2 (en) 2020-06-17 2023-03-21 Toyota Research Institute, Inc. Systems and methods for scenario marker infrastructure
CN111815989A (en) * 2020-06-19 2020-10-23 勇鸿(重庆)信息科技有限公司 C-V2X technology-based road accident rescue method and system
CN111765903B (en) * 2020-06-29 2022-08-09 阿波罗智能技术(北京)有限公司 Test method, device, electronic device and medium for automatic driving vehicle
US11341866B2 (en) 2020-06-30 2022-05-24 Toyota Research Institute, Inc. Systems and methods for training a driver about automated driving operation
EP4165476A1 (en) 2020-07-01 2023-04-19 May Mobility, Inc. Method and system for dynamically curating autonomous vehicle policies
DE102020209291A1 (en) * 2020-07-23 2022-01-27 Robert Bosch Gesellschaft mit beschränkter Haftung Method and emergency response system for operating an autonomous vehicle in an emergency event
US20220028187A1 (en) * 2020-07-23 2022-01-27 Denso International America, Inc. Method and system of managing a vehicle abnormality of a fleet vehicle
US11807272B2 (en) 2020-07-28 2023-11-07 Toyota Research Institute, Inc. Systems and methods for multiple algorithm selection
US20220044551A1 (en) * 2020-08-10 2022-02-10 Ross David Sheckler Safety system and safety apparatus
US20220051340A1 (en) * 2020-08-14 2022-02-17 GM Global Technology Operations LLC System and Method Using Crowd-Sourced Data to Evaluate Driver Performance
US11336727B2 (en) * 2020-08-18 2022-05-17 Geotab Inc. Specialized casing unit detection for asset tracking devices
DE102020210920A1 (en) * 2020-08-28 2022-03-03 Brose Fahrzeugteile Se & Co. Kommanditgesellschaft, Bamberg Method for adjusting an adjustment part on a vehicle and storing signal and measured value curves for subsequent testing
JP2022055883A (en) * 2020-09-29 2022-04-08 株式会社Subaru vehicle
US11387985B2 (en) 2020-10-01 2022-07-12 Toyota Motor North America, Inc. Transport occupant data delivery
CN112365685A (en) * 2020-10-16 2021-02-12 中国民用航空总局第二研究所 Method, device and system for managing health state of target vehicle in airport scene
US20230237584A1 (en) * 2020-10-29 2023-07-27 BlueOwl, LLC Systems and methods for evaluating vehicle insurance claims
US20220138810A1 (en) * 2020-10-30 2022-05-05 Toyota Motor North America, Inc. Transport use determination
KR20220064599A (en) 2020-11-12 2022-05-19 주식회사 가린시스템 System and method of providing active service using remote vehicle starter based on big data analysis
JP2023553980A (en) 2020-12-14 2023-12-26 メイ モビリティー,インコーポレイテッド Autonomous vehicle safety platform system and method
EP4264181A1 (en) 2020-12-17 2023-10-25 May Mobility, Inc. Method and system for dynamically updating an environmental representation of an autonomous agent
US11794764B2 (en) 2020-12-21 2023-10-24 Toyota Motor North America, Inc. Approximating a time of an issue
US11554671B2 (en) 2020-12-21 2023-01-17 Toyota Motor North America, Inc. Transport data display cognition
CN112687173B (en) * 2020-12-25 2022-06-21 安徽机电职业技术学院 Automobile collision demonstration platform based on active and passive safety collaborative optimization
AU2021102368A4 (en) * 2021-01-22 2021-06-24 4AI Systems Holdings Pty Ltd A Sensor Device for Vehicles
CN112896388B (en) * 2021-02-04 2022-11-22 上海钧正网络科技有限公司 Riding safety detection method and device, electronic equipment and storage medium
US11544795B2 (en) 2021-02-09 2023-01-03 Futurity Group, Inc. Automatically labeling data using natural language processing
DE102021103732A1 (en) 2021-02-17 2022-08-18 Cariad Se Driver assistance system, motor vehicle and method for automatic longitudinal guidance of a motor vehicle
DE102021201553A1 (en) 2021-02-18 2022-08-18 Volkswagen Aktiengesellschaft Method and emergency call system for transmitting emergency data relating to a vehicle
CN112905477B (en) * 2021-03-15 2022-12-13 苏州智行众维智能科技有限公司 Automatic driving simulation test data release system, method, device and equipment
US11926282B2 (en) 2021-03-16 2024-03-12 Ford Global Technologies, Llc Systems and methods for random vehicle movement for vehicle safety
EP4314708A1 (en) 2021-04-02 2024-02-07 May Mobility, Inc. Method and system for operating an autonomous agent with incomplete environmental information
JP2022172950A (en) * 2021-05-07 2022-11-17 トヨタ自動車株式会社 Remote support management system, remote support management method, and remote support management program
US11820387B2 (en) * 2021-05-10 2023-11-21 Qualcomm Incorporated Detecting driving behavior of vehicles
WO2022244285A1 (en) * 2021-05-19 2022-11-24 パナソニックIpマネジメント株式会社 Insurance premium calculation method, program and insurance premium calculation system
US11565717B2 (en) * 2021-06-02 2023-01-31 May Mobility, Inc. Method and system for remote assistance of an autonomous agent
US20220388530A1 (en) * 2021-06-07 2022-12-08 Toyota Motor North America, Inc. Transport limitations from malfunctioning sensors
US11697069B1 (en) 2021-08-17 2023-07-11 BlueOwl, LLC Systems and methods for presenting shared in-game objectives in virtual games
US11896903B2 (en) 2021-08-17 2024-02-13 BlueOwl, LLC Systems and methods for generating virtual experiences for a virtual game
US11504622B1 (en) * 2021-08-17 2022-11-22 BlueOwl, LLC Systems and methods for generating virtual encounters in virtual games
US20230075217A1 (en) * 2021-09-08 2023-03-09 Centinel Inc. Method for evaluation and payout of parametric risk-coverage for hard-to-insure risks using distributed ledger and associated system
FR3127313A1 (en) * 2021-09-17 2023-03-24 Institut De Recherche Technologique Systemx Method for evaluating the performance of a driving model for a vehicle
US11891078B1 (en) 2021-09-29 2024-02-06 Zoox, Inc. Vehicle operating constraints
US11891076B1 (en) * 2021-09-29 2024-02-06 Zoox, Inc. Manual operation vehicle constraints
US20230121913A1 (en) * 2021-10-19 2023-04-20 Volvo Car Corporation Intelligent messaging framework for vehicle ecosystem communication
CN113911015B (en) * 2021-12-06 2022-04-29 杭州网兰科技有限公司 Faulty vehicle processing guidance method, computer device, and computer storage medium
GB202200011D0 (en) * 2022-01-02 2022-02-16 Delev Tsvetan Ivanov Tension index data system
CN114419927A (en) * 2022-01-25 2022-04-29 中国重汽集团济南动力有限公司 Pedestrian speed control method and system for pedestrian test
US20230234592A1 (en) * 2022-01-26 2023-07-27 Wireless Advanced Vehicle Electrification, Llc Electric vehicle fleet optimization based on driver behavior
WO2023152239A1 (en) * 2022-02-09 2023-08-17 Swiss Reinsurance Company Ltd. Digital framework for autonomous or partially autonomous vehicle and/or electric vehicles risk exposure monitoring, measuring and exposure cover pricing, and method thereof
US11727795B1 (en) 2022-02-11 2023-08-15 Hayden Ai Technologies, Inc. Methods and systems for trusted management of traffic violation data using a distributed ledger
WO2023154568A1 (en) * 2022-02-14 2023-08-17 May Mobility, Inc. Method and system for conditional operation of an autonomous agent
DE102022001241A1 (en) 2022-04-12 2023-10-12 Mercedes-Benz Group AG Method of operating a vehicle
DE102022119179A1 (en) 2022-08-01 2024-02-01 Dr. Ing. H.C. F. Porsche Aktiengesellschaft Method, system and computer program product for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function
CN115294801B (en) * 2022-08-03 2023-09-22 广东凯莎科技有限公司 Vehicle-mounted network communication method

Citations (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH063723A (en) * 1992-06-23 1994-01-14 Olympus Optical Co Ltd Image processor
WO2000013947A2 (en) * 1998-09-09 2000-03-16 Siemens Automotive Corporation Remote control key system having keyless entry functions and a vehicle immobilizing function in common keyhead
US20030069761A1 (en) * 2001-10-10 2003-04-10 Increment P Corporation, Shuji Kawakami, And Nobuhiro Shoji System for taking out insurance policy, method of taking out insurance policy, server apparatus and terminal apparatus
US20030089183A1 (en) * 2001-11-13 2003-05-15 Jacobsen Robert A. Apparatus and method for non-destructive inspection of large structures
US20040088089A1 (en) * 2000-09-20 2004-05-06 Wolf-Dieterich Bauer Device and method for detecting driving data of a motor vehicle comprising an electronically controllable drive train
US20050043856A1 (en) * 2002-10-04 2005-02-24 Bautista Dexter V. Ultrasonic sensor for detecting the dispensing of a product
FR2863214A1 (en) * 2003-12-05 2005-06-10 Toyota Motor Co Ltd DEVICE AND METHOD FOR CONTROLLING DECELERATION FOR A VEHICLE
US20070088454A1 (en) * 2004-10-25 2007-04-19 Ford Motor Company System and method for troubleshooting a machine
WO2008154097A1 (en) * 2007-05-07 2008-12-18 The Boeing Company Health management of single or multi-platform systems
US7525417B1 (en) * 2008-05-16 2009-04-28 International Business Machines Corporation Method and system for transitive turn signal and braking indication
US20090109037A1 (en) * 2000-08-11 2009-04-30 Telanon, Inc. Automated consumer to business electronic marketplace system
US20120046841A1 (en) * 2009-04-21 2012-02-23 Zf Friedrichshafen Ag Method for operating a vehicle having a freewheel mode or a rolling mode
US20120109692A1 (en) * 2010-05-17 2012-05-03 The Travelers Indemnity Company Monitoring customer-selected vehicle parameters in accordance with customer preferences
US20120123806A1 (en) * 2009-12-31 2012-05-17 Schumann Jr Douglas D Systems and methods for providing a safety score associated with a user location
US8560165B2 (en) * 2012-01-17 2013-10-15 GM Global Technology Operations LLC Co-operative on-board and off-board component and system diagnosis and prognosis
US8630806B1 (en) * 2011-10-20 2014-01-14 Google Inc. Image processing for vehicle control
US8712909B1 (en) * 2011-02-23 2014-04-29 United Services Automobile Association Systems and methods for managing fleet services
DE102013200391A1 (en) * 2013-01-14 2014-07-17 Robert Bosch Gmbh Method for adaptive cruise control of motor vehicle, involves automatically regulating actual speed of motor vehicle to predetermined target speed and automatically changing target speed and target distance
US9019092B1 (en) * 2013-03-08 2015-04-28 Allstate Insurance Company Determining whether a vehicle is parked for automated accident detection, fault attribution, and claims processing
US9274525B1 (en) * 2012-09-28 2016-03-01 Google Inc. Detecting sensor degradation by actively controlling an autonomous vehicle
US20170190335A1 (en) * 2013-04-26 2017-07-06 Carla R. Gillett Vehicle Comprising Autonomous Steering Column System
US10705533B1 (en) * 2005-05-31 2020-07-07 Richard Anthony Bishel Autonomous lawnmower
US10713726B1 (en) * 2013-01-13 2020-07-14 United Services Automobile Association (Usaa) Determining insurance policy modifications using informatic sensor data
US11716488B2 (en) * 2019-09-20 2023-08-01 Qualcomm Incorporated Subpicture signaling in high-level syntax for video coding
US20230290251A1 (en) * 2012-09-27 2023-09-14 Waymo Llc Determining changes in a driving environment based on vehicle behavior

Family Cites Families (896)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US2042010A (en) 1933-08-25 1936-05-26 Firestone Tire & Rubber Co Fabric spreader
US4218763A (en) 1978-08-04 1980-08-19 Brailsford Lawrence J Electronic alarm signaling system
JPS56105967U (en) 1980-01-15 1981-08-18
JPS5750097A (en) 1980-09-08 1982-03-24 Nissan Motor Automotive warning device
US7663502B2 (en) 1992-05-05 2010-02-16 Intelligent Technologies International, Inc. Asset system control arrangement and method
US5367456A (en) 1985-08-30 1994-11-22 Texas Instruments Incorporated Hierarchical control system for automatically guided vehicles
US4833469A (en) * 1987-08-03 1989-05-23 David Constant V Obstacle proximity detector for moving vehicles and method for use thereof
US5617365A (en) * 1988-10-07 1997-04-01 Hitachi, Ltd. Semiconductor device having redundancy circuit
US5214582C1 (en) * 1991-01-30 2001-06-26 Edge Diagnostic Systems Interactive diagnostic system for an automobile vehicle and method
US7596242B2 (en) 1995-06-07 2009-09-29 Automotive Technologies International, Inc. Image processing for vehicular applications
US5368484A (en) 1992-05-22 1994-11-29 Atari Games Corp. Vehicle simulator with realistic operating feedback
GB2268608A (en) 1992-06-10 1994-01-12 Norm Pacific Automat Corp Vehicle accident prevention and recording system
US5453939A (en) 1992-09-16 1995-09-26 Caterpillar Inc. Computerized diagnostic and monitoring system
US5436839A (en) * 1992-10-26 1995-07-25 Martin Marietta Corporation Navigation module for a semi-autonomous vehicle
US5368464A (en) * 1992-12-31 1994-11-29 Eastman Kodak Company Ultrasonic apparatus for cutting and placing individual chips of light lock material
JP3269153B2 (en) 1993-01-06 2002-03-25 三菱自動車工業株式会社 Arousal level determination device
JPH06197888A (en) 1993-01-06 1994-07-19 Mitsubishi Motors Corp Doze warning device for vehicle
US5363298A (en) 1993-04-29 1994-11-08 The United States Of America As Represented By The Secretary Of The Navy Controlled risk decompression meter
US7397363B2 (en) * 1993-06-08 2008-07-08 Raymond Anthony Joao Control and/or monitoring apparatus and method
US5983161A (en) 1993-08-11 1999-11-09 Lemelson; Jerome H. GPS vehicle collision avoidance warning and control system and method
US5515026A (en) 1994-01-28 1996-05-07 Ewert; Roger D. Total alert driver safety system
US7421321B2 (en) 1995-06-07 2008-09-02 Automotive Technologies International, Inc. System for obtaining vehicular information
US5626362A (en) 1994-06-07 1997-05-06 Interactive Driving Systems, Inc. Simulator for teaching vehicle speed control and skid recovery techniques
ES2108613B1 (en) 1994-09-01 1998-08-01 Perez Salvador Minguijon SYSTEM TO ASSESS RISK IN AUTOMOBILE VEHICLES.
US5499182A (en) 1994-12-07 1996-03-12 Ousborne; Jeffrey Vehicle driver performance monitoring system
US5689241A (en) 1995-04-24 1997-11-18 Clarke, Sr.; James Russell Sleep detection and driver alert apparatus
US8036788B2 (en) 1995-06-07 2011-10-11 Automotive Technologies International, Inc. Vehicle diagnostic or prognostic message transmission systems and methods
US20080161989A1 (en) 1995-06-07 2008-07-03 Automotive Technologies International, Inc. Vehicle Diagnostic or Prognostic Message Transmission Systems and Methods
US7085637B2 (en) * 1997-10-22 2006-08-01 Intelligent Technologies International, Inc. Method and system for controlling a vehicle
US5835008A (en) 1995-11-28 1998-11-10 Colemere, Jr.; Dale M. Driver, vehicle and traffic information system
US8140358B1 (en) 1996-01-29 2012-03-20 Progressive Casualty Insurance Company Vehicle monitoring system
US8090598B2 (en) 1996-01-29 2012-01-03 Progressive Casualty Insurance Company Monitoring system for determining and communicating a cost of insurance
US5797134A (en) * 1996-01-29 1998-08-18 Progressive Casualty Insurance Company Motor vehicle monitoring system for determining a cost of insurance
US5710503A (en) 1996-02-01 1998-01-20 Aims Systems, Inc. On-line battery monitoring system with defective cell detection capability
US6400835B1 (en) 1996-05-15 2002-06-04 Jerome H. Lemelson Taillight mounted vehicle security system employing facial recognition using a reflected image
US6265978B1 (en) 1996-07-14 2001-07-24 Atlas Researches, Ltd. Method and apparatus for monitoring states of consciousness, drowsiness, distress, and performance
JP3272960B2 (en) 1996-08-19 2002-04-08 株式会社データ・テック Driving recorder and vehicle operation analyzer
US6271745B1 (en) 1997-01-03 2001-08-07 Honda Giken Kogyo Kabushiki Kaisha Keyless user identification and authorization system for a motor vehicle
GB9700090D0 (en) 1997-01-04 1997-02-19 Horne James A Sleepiness detection for vehicle driver
US6253129B1 (en) 1997-03-27 2001-06-26 Tripmaster Corporation System for monitoring vehicle efficiency and vehicle and driver performance
US7870010B2 (en) 1997-07-31 2011-01-11 Raymond Anthony Joao Apparatus and method for processing lease insurance information
US6275231B1 (en) 1997-08-01 2001-08-14 American Calcar Inc. Centralized control and management system for automobiles
US7647180B2 (en) 1997-10-22 2010-01-12 Intelligent Technologies International, Inc. Vehicular intersection management techniques
US7979172B2 (en) 1997-10-22 2011-07-12 Intelligent Technologies International, Inc. Autonomous vehicle travel control systems and methods
US8965677B2 (en) 1998-10-22 2015-02-24 Intelligent Technologies International, Inc. Intra-vehicle information conveyance system and method
US8255144B2 (en) 1997-10-22 2012-08-28 Intelligent Technologies International, Inc. Intra-vehicle information conveyance system and method
US7983802B2 (en) 1997-10-22 2011-07-19 Intelligent Technologies International, Inc. Vehicular environment scanning techniques
US8209120B2 (en) 1997-10-22 2012-06-26 American Vehicular Sciences Llc Vehicular map database management techniques
US7791503B2 (en) 1997-10-22 2010-09-07 Intelligent Technologies International, Inc. Vehicle to infrastructure information conveyance system and method
US7979173B2 (en) 1997-10-22 2011-07-12 Intelligent Technologies International, Inc. Autonomous vehicle travel control systems and methods
EP0913751B1 (en) 1997-11-03 2003-09-03 Volkswagen Aktiengesellschaft Autonomous vehicle and guiding method for an autonomous vehicle
US6285931B1 (en) 1998-02-05 2001-09-04 Denso Corporation Vehicle information communication system and method capable of communicating with external management station
US20010005217A1 (en) 1998-06-01 2001-06-28 Hamilton Jeffrey Allen Incident recording information transfer device
US6141611A (en) 1998-12-01 2000-10-31 John J. Mackey Mobile vehicle accident data system
WO2000028410A1 (en) 1998-11-06 2000-05-18 Phoenix Group, Inc. Mobile vehicle accident data system
US6704434B1 (en) 1999-01-27 2004-03-09 Suzuki Motor Corporation Vehicle driving information storage apparatus and vehicle driving information storage method
US7539628B2 (en) * 2000-03-21 2009-05-26 Bennett James D Online purchasing system supporting buyer affordability screening
US6570609B1 (en) 1999-04-22 2003-05-27 Troy A. Heien Method and apparatus for monitoring operation of a motor vehicle
JP3937205B2 (en) * 1999-04-28 2007-06-27 ボッシュ株式会社 Brake system
AT410531B (en) 1999-05-25 2003-05-26 Bernard Ing Douet METHOD AND SYSTEM FOR AUTOMATIC DETECTION OR MONITORING THE POSITION OF AT LEAST ONE RAIL VEHICLE
US6983313B1 (en) 1999-06-10 2006-01-03 Nokia Corporation Collaborative location server/system
DE19934862C1 (en) 1999-07-24 2001-03-01 Bosch Gmbh Robert Navigation method and navigation system for motor vehicles
US7124088B2 (en) 1999-07-30 2006-10-17 Progressive Casualty Insurance Company Apparatus for internet on-line insurance policy service
US6790228B2 (en) * 1999-12-23 2004-09-14 Advanced Cardiovascular Systems, Inc. Coating for implantable devices and a method of forming the same
US20020049535A1 (en) 1999-09-20 2002-04-25 Ralf Rigo Wireless interactive voice-actuated mobile telematics system
US6661345B1 (en) 1999-10-22 2003-12-09 The Johns Hopkins University Alertness monitoring system
US6246933B1 (en) 1999-11-04 2001-06-12 BAGUé ADOLFO VAEZA Traffic accident data recorder and traffic accident reproduction system and method
US7110947B2 (en) 1999-12-10 2006-09-19 At&T Corp. Frame erasure concealment technique for a bitstream-based feature extractor
US6298290B1 (en) 1999-12-30 2001-10-02 Niles Parts Co., Ltd. Memory apparatus for vehicle information data
US8103526B1 (en) 2000-03-07 2012-01-24 Insweb Corporation System and method for flexible insurance rating calculation
US6553354B1 (en) * 2000-04-04 2003-04-22 Ford Motor Company Method of probabilistically modeling variables
EP1275217B1 (en) 2000-04-21 2012-12-26 Broadcom Corporation Performance indicator for a high-speed communication system
KR20010103998A (en) 2000-05-12 2001-11-24 이계안 Curren leakage preventy system and method for hybrid electric vehicle
US6323761B1 (en) 2000-06-03 2001-11-27 Sam Mog Son Vehicular security access system
US6765495B1 (en) 2000-06-07 2004-07-20 Hrl Laboratories, Llc Inter vehicle communication system
US6477117B1 (en) 2000-06-30 2002-11-05 International Business Machines Corporation Alarm interface for a smart watch
US20020103622A1 (en) 2000-07-17 2002-08-01 Burge John R. Decision-aid system based on wirelessly-transmitted vehicle crash sensor information
US20020111725A1 (en) 2000-07-17 2002-08-15 Burge John R. Method and apparatus for risk-related use of vehicle communication system data
US7904219B1 (en) 2000-07-25 2011-03-08 Htiip, Llc Peripheral access devices and sensors for use with vehicle telematics devices and systems
US20020016655A1 (en) 2000-08-01 2002-02-07 Joao Raymond Anthony Apparatus and method for processing and/or for providing vehicle information and/or vehicle maintenance information
SE0002804D0 (en) 2000-08-01 2000-08-01 Promind Ab Technology for continuously mapping the behavior / behavior of the vehicle / driver to determine the reaction coefficient of the vehicle or the vehicle. driver's skill coefficient, and device for graphical presentation of these coefficients
US20050091175A9 (en) 2000-08-11 2005-04-28 Telanon, Inc. Automated consumer to business electronic marketplace system
US7349860B1 (en) 2000-08-24 2008-03-25 Creative Innovators Associates, Llc Insurance incentive program having a term of years for promoting the purchase or lease of an automobile
US7941258B1 (en) 2000-08-31 2011-05-10 Strategic Design Federation W, Inc. Automobile monitoring for operation analysis
US6556905B1 (en) 2000-08-31 2003-04-29 Lisa M. Mittelsteadt Vehicle supervision and monitoring
US9151692B2 (en) 2002-06-11 2015-10-06 Intelligent Technologies International, Inc. Asset monitoring system using multiple imagers
US7135961B1 (en) 2000-09-29 2006-11-14 International Business Machines Corporation Method and system for providing directions for driving
JP3834463B2 (en) 2000-10-13 2006-10-18 株式会社日立製作所 In-vehicle failure alarm reporting system
US7565230B2 (en) 2000-10-14 2009-07-21 Temic Automotive Of North America, Inc. Method and apparatus for improving vehicle operator performance
US6909947B2 (en) 2000-10-14 2005-06-21 Motorola, Inc. System and method for driver performance improvement
AU2002245191A1 (en) 2000-10-26 2002-07-24 Digimarc Corporation Method and system for internet access
US6727800B1 (en) 2000-11-01 2004-04-27 Iulius Vivant Dutu Keyless system for entry and operation of a vehicle
US6879969B2 (en) 2001-01-21 2005-04-12 Volvo Technological Development Corporation System and method for real-time recognition of driving patterns
US20020103678A1 (en) 2001-02-01 2002-08-01 Burkhalter Swinton B. Multi-risk insurance system and method
US6964023B2 (en) 2001-02-05 2005-11-08 International Business Machines Corporation System and method for multi-modal focus detection, referential ambiguity resolution and mood classification using multi-modal input
US20020107716A1 (en) 2001-02-07 2002-08-08 Kevin Callahan Methods and apparatus for scheduling an in-home appliance repair service
US20020146667A1 (en) 2001-02-14 2002-10-10 Safe Drive Technologies, Llc Staged-learning process and system for situational awareness training using integrated media
JP2002318844A (en) 2001-02-15 2002-10-31 Hitachi Ltd Method for managing vehicle
JP2002259708A (en) 2001-03-06 2002-09-13 Toyota Motor Corp Vehicular insurance bill calculating system, on-vehicle device, and server device
US7027621B1 (en) 2001-03-15 2006-04-11 Mikos, Ltd. Method and apparatus for operator condition monitoring and assessment
US7266532B2 (en) 2001-06-01 2007-09-04 The General Hospital Corporation Reconfigurable autonomous device networks
US7042347B2 (en) 2001-06-19 2006-05-09 Cherouny Peter H Electronic programmable speed limiter
US6579233B2 (en) 2001-07-06 2003-06-17 Science Applications International Corp. System and method for evaluating task effectiveness based on sleep pattern
US7298387B2 (en) 2001-08-22 2007-11-20 Polaroid Corporation Thermal response correction system
NZ531530A (en) 2001-09-06 2007-01-26 Northwest Biotherapeutics Inc Compositions and methods for priming monocytic dendritic cells and T cells for TH-1 response using BCG and IFN gamma
JP3775494B2 (en) 2001-09-21 2006-05-17 日本電気株式会社 Information processing apparatus for billing system and billing information collecting method
US6701234B1 (en) 2001-10-18 2004-03-02 Andrew John Vogelsang Portable motion recording device for motor vehicles
US20030200123A1 (en) 2001-10-18 2003-10-23 Burge John R. Injury analysis system and method for insurance claims
US6473000B1 (en) * 2001-10-24 2002-10-29 James Secreet Method and apparatus for measuring and recording vehicle speed and for storing related data
JP4227743B2 (en) 2001-11-19 2009-02-18 株式会社デンソー Anti-theft system
US7395219B2 (en) 2001-12-08 2008-07-01 Kenneth Ray Strech Insurance on demand transaction management system
US6741168B2 (en) 2001-12-13 2004-05-25 Samsung Electronics Co., Ltd. Method and apparatus for automated collection and transfer of collision information
EP1324274A3 (en) * 2001-12-28 2005-11-02 Matsushita Electric Industrial Co., Ltd. Vehicle information recording system
US7386376B2 (en) 2002-01-25 2008-06-10 Intelligent Mechatronic Systems, Inc. Vehicle visual and non-visual data recording system
US6944536B2 (en) 2002-02-01 2005-09-13 Medaire, Inc. Method and system for identifying medical facilities along a travel route
US6721632B2 (en) 2002-02-05 2004-04-13 International Business Machines Corporation Wireless exchange between vehicle-borne communications systems
CN101115646B (en) 2002-03-19 2011-11-16 汽车系统实验室公司 Vehicle rollover detection system
US20030182183A1 (en) 2002-03-20 2003-09-25 Christopher Pribe Multi-car-pool organization method
JP2003276470A (en) 2002-03-22 2003-09-30 Nissan Motor Co Ltd Information presentation control device
US20040005927A1 (en) 2002-04-22 2004-01-08 Bonilla Victor G. Facility for remote computer controlled racing
US20040077285A1 (en) 2002-04-22 2004-04-22 Bonilla Victor G. Method, apparatus, and system for simulating visual depth in a concatenated image of a remote field of action
US7290275B2 (en) 2002-04-29 2007-10-30 Schlumberger Omnes, Inc. Security maturity assessment method
US7559293B2 (en) * 2002-06-04 2009-07-14 Bradford White Corporation High efficiency water heater
US9082237B2 (en) 2002-06-11 2015-07-14 Intelligent Technologies International, Inc. Vehicle access and security based on biometrics
US20130267194A1 (en) 2002-06-11 2013-10-10 American Vehicular Sciences Llc Method and System for Notifying a Remote Facility of an Accident Involving a Vehicle
US8035508B2 (en) 2002-06-11 2011-10-11 Intelligent Technologies International, Inc. Monitoring using cellular phones
JP2004017889A (en) 2002-06-19 2004-01-22 Advics:Kk Automatic brake
US20040019539A1 (en) 2002-07-25 2004-01-29 3Com Corporation Prepaid billing system for wireless data networks
US20040198441A1 (en) 2002-07-29 2004-10-07 George Cooper Wireless communication device and method
US7102496B1 (en) 2002-07-30 2006-09-05 Yazaki North America, Inc. Multi-sensor integration for a vehicle
KR100489357B1 (en) * 2002-08-08 2005-05-16 주식회사 하이닉스반도체 Cell array structure in nonvolatile ferroelectric memory device and scheme for operating the same
US6795759B2 (en) 2002-08-26 2004-09-21 International Business Machines Corporation Secure logging of vehicle data
US7676062B2 (en) 2002-09-03 2010-03-09 Automotive Technologies International Inc. Image processing for vehicular applications applying image comparisons
DE10240838A1 (en) 2002-09-04 2004-03-18 Robert Bosch Gmbh Motor vehicle accident reconstruction method, in which driving data for use in accident reconstruction is captured from existing onboard control electronics and used to generate a dynamic 3D kinematic model which is recorded
US20060272704A1 (en) 2002-09-23 2006-12-07 R. Giovanni Fima Systems and methods for monitoring and controlling fluid consumption
JP3699434B2 (en) 2002-10-03 2005-09-28 三菱電機株式会社 Vehicle anti-theft device
US6832141B2 (en) 2002-10-25 2004-12-14 Davis Instruments Module for monitoring vehicle operation through onboard diagnostic port
JP3829793B2 (en) 2002-11-06 2006-10-04 株式会社デンソー Emergency call device
US7202792B2 (en) 2002-11-11 2007-04-10 Delphi Technologies, Inc. Drowsiness detection system and method
KR100480727B1 (en) 2002-11-26 2005-04-07 엘지전자 주식회사 Apparatus for controlling heater of a dryer
US7725334B2 (en) 2002-11-27 2010-05-25 Computer Sciences Corporation Computerized method and system for estimating liability for an accident using dynamic generation of questions
WO2004059996A1 (en) 2002-12-27 2004-07-15 Nokia Corporation Location based services for mobile communication terminals
SE526913C2 (en) * 2003-01-02 2005-11-15 Arnex Navigation Systems Ab Procedure in the form of intelligent functions for vehicles and automatic loading machines regarding mapping of terrain and material volumes, obstacle detection and control of vehicles and work tools
US20040158476A1 (en) 2003-02-06 2004-08-12 I-Sim, Llc Systems and methods for motor vehicle learning management
JP4235465B2 (en) 2003-02-14 2009-03-11 本田技研工業株式会社 Riding simulation equipment
WO2004082479A1 (en) 2003-02-24 2004-09-30 Electronic Navigation Research Institute, Independent Administrative Institution Psychosomatic state determination system
US7138922B2 (en) 2003-03-18 2006-11-21 Ford Global Technologies, Llc Drowsy driver monitoring and prevention system
DE10314119A1 (en) 2003-03-28 2004-10-21 Dieter Dr. Bastian Process for determining an integral risk potential for a road user and device for carrying out the process
US7587287B2 (en) 2003-04-04 2009-09-08 Abbott Diabetes Care Inc. Method and system for transferring analyte test data
US6970102B2 (en) 2003-05-05 2005-11-29 Transol Pty Ltd Traffic violation detection, recording and evidence processing system
US6931309B2 (en) 2003-05-06 2005-08-16 Innosurance, Inc. Motor vehicle operating data collection and analysis
US20040226043A1 (en) 2003-05-07 2004-11-11 Autodesk, Inc. Location enabled television
US7071821B2 (en) 2003-05-14 2006-07-04 Bellsouth Intellectual Property Corporation Method and system for alerting a person to a situation
WO2004104968A1 (en) 2003-05-15 2004-12-02 Landsonar, Inc. System and method for evaluating vehicle and operator performance
WO2004114055A2 (en) 2003-05-23 2004-12-29 Nnt, Inc. An enterprise resource planning system with integrated vehicle diagnostic and information system
CN1802273B (en) 2003-06-06 2010-05-12 沃尔沃技术公司 Method and arrangement for controlling vehicular subsystems based on interpreted driver activity
US7292152B2 (en) 2003-06-12 2007-11-06 Temic Automotive Of North America, Inc. Method and apparatus for classifying vehicle operator activity state
US20040260579A1 (en) 2003-06-19 2004-12-23 Tremiti Kimberly Irene Technique for providing automobile insurance
US7115230B2 (en) 2003-06-26 2006-10-03 Intel Corporation Hydrodynamic focusing devices
US8275417B2 (en) 2003-06-27 2012-09-25 Powerwave Technologies, Inc. Flood evacuation system for subterranean telecommunications vault
US7206697B2 (en) 2003-10-14 2007-04-17 Delphi Technologies, Inc. Driver adaptive collision warning system
JP2005096744A (en) 2003-09-01 2005-04-14 Matsushita Electric Ind Co Ltd Occupant certifying system
US9311676B2 (en) 2003-09-04 2016-04-12 Hartford Fire Insurance Company Systems and methods for analyzing sensor data
US7711584B2 (en) 2003-09-04 2010-05-04 Hartford Fire Insurance Company System for reducing the risk associated with an insured building structure through the incorporation of selected technologies
US7424414B2 (en) 2003-09-05 2008-09-09 Road Safety International, Inc. System for combining driving simulators and data acquisition systems and methods of use thereof
US7797107B2 (en) 2003-09-16 2010-09-14 Zvi Shiller Method and system for providing warnings concerning an imminent vehicular collision
US7095336B2 (en) 2003-09-23 2006-08-22 Optimus Corporation System and method for providing pedestrian alerts
US7069118B2 (en) 2003-09-30 2006-06-27 International Business Machines Corporation Apparatus, system, and method for exchanging vehicle identification data
US7542915B2 (en) 2003-09-30 2009-06-02 The Boeing Company System of charging for automobile insurance
WO2005050849A2 (en) 2003-10-01 2005-06-02 Laird Mark D Wireless virtual campus escort system
US7092799B2 (en) 2003-10-10 2006-08-15 General Motors Corporation Method and system for remotely inventorying electronic modules installed in a vehicle
US20050088521A1 (en) 2003-10-22 2005-04-28 Mobile-Vision Inc. In-car video system using flash memory as a recording medium
US7023333B2 (en) 2003-10-22 2006-04-04 L-3 Communications Mobile Vision, Inc. Automatic activation of an in-car video recorder using a vehicle speed sensor signal
US20050093684A1 (en) 2003-10-30 2005-05-05 Cunnien Cole J. Frame assembly for a license plate
US7877275B2 (en) 2003-11-13 2011-01-25 General Motors Llc System and method for maintaining and providing personal information in real time
CN1882458A (en) * 2003-11-14 2006-12-20 大陆-特韦斯贸易合伙股份公司及两合公司 Method and device for reducing damage caused by an accident
US20050108910A1 (en) 2003-11-22 2005-05-26 Esparza Erin A. Apparatus and method for promoting new driver awareness
US20060074558A1 (en) * 2003-11-26 2006-04-06 Williamson Walton R Fault-tolerant system, apparatus and method
US7389178B2 (en) 2003-12-11 2008-06-17 Greenroad Driving Technologies Ltd. System and method for vehicle driver behavior analysis and evaluation
US7783505B2 (en) 2003-12-30 2010-08-24 Hartford Fire Insurance Company System and method for computerized insurance rating
JP4140720B2 (en) 2004-01-14 2008-08-27 三菱電機株式会社 Vehicle behavior reproduction system
JPWO2005083605A1 (en) 2004-02-26 2008-01-17 あいおい損害保険株式会社 Insurance premium calculation device, insurance premium calculation program, insurance premium calculation method, and insurance premium calculation system
US7680694B2 (en) 2004-03-11 2010-03-16 American Express Travel Related Services Company, Inc. Method and apparatus for a user to shop online in a three dimensional virtual reality setting
US7482911B2 (en) 2004-03-11 2009-01-27 Bayerische Motoren Werke Aktiengesellschaft Process for the output of information in a vehicle
US8694475B2 (en) 2004-04-03 2014-04-08 Altusys Corp. Method and apparatus for situation-based management
US7847800B2 (en) * 2004-04-16 2010-12-07 Apple Inc. System for emulating graphics operations
US7761351B2 (en) 2004-04-29 2010-07-20 Ford Motor Company Method and system for assessing the risk of a vehicle dealership defaulting on a financial obligation
US7895054B2 (en) 2004-05-06 2011-02-22 Humana Inc. Pharmacy personal care account
US20060031103A1 (en) 2004-08-06 2006-02-09 Henry David S Systems and methods for diagram data collection
US20100143872A1 (en) 2004-09-03 2010-06-10 Gold Cross Benefits Corporation Driver safety program based on behavioral profiling
JP4596863B2 (en) 2004-09-03 2010-12-15 コマツ工機株式会社 Inspection device and method for scratches on workpiece surface
US20060053038A1 (en) 2004-09-08 2006-03-09 Warren Gregory S Calculation of driver score based on vehicle operation
US7176813B2 (en) 2004-09-10 2007-02-13 Xanavi Informatics Corporation System and method for processing and displaying traffic information in an automotive navigation system
US7519362B2 (en) 2004-09-13 2009-04-14 Laperch Richard C Personal wireless gateway and method for implementing the same
GB2432922B (en) 2004-10-22 2009-09-02 Irobot Corp Systems and methods for control of a vehicle
US7499776B2 (en) 2004-10-22 2009-03-03 Irobot Corporation Systems and methods for control of an unmanned ground vehicle
US7499774B2 (en) * 2004-10-22 2009-03-03 Irobot Corporation System and method for processing safety signals in an autonomous vehicle
US7890355B2 (en) 2004-10-29 2011-02-15 Milemeter, Inc. System and method for the assessment, pricing, and provisioning of distance-based vehicle insurance
US7987103B2 (en) 2004-10-29 2011-07-26 Milemeter, Inc. System and method for the assessment, pricing, and provisioning of distance-based vehicle insurance
US20070299700A1 (en) 2004-10-29 2007-12-27 Milemeter, Inc. System and Method for Assessing Earned Premium for Distance-Based Vehicle Insurance
US7865378B2 (en) 2004-10-29 2011-01-04 Milemeter, Inc. System and method for the assessment, pricing, and provisioning of distance-based vehicle insurance
US7991629B2 (en) 2004-10-29 2011-08-02 Milemeter, Inc. System and method for the assessment, pricing, and provisioning of distance-based vehicle insurance
US7348895B2 (en) * 2004-11-03 2008-03-25 Lagassey Paul J Advanced automobile accident detection, data recordation and reporting system
US7253724B2 (en) 2004-11-05 2007-08-07 Ford Global Technologies, Inc. Vehicle pre-impact sensing and control system with driver response feedback
WO2006051436A1 (en) 2004-11-11 2006-05-18 Philips Intellectual Property & Standards Gmbh Device and method for event-triggered communication between and among a plurality of nodes
EP1662456B1 (en) 2004-11-24 2008-10-22 Harman Becker Automotive Systems GmbH Driver information system
US7908080B2 (en) 2004-12-31 2011-03-15 Google Inc. Transportation routing
US7937278B1 (en) 2005-01-18 2011-05-03 Allstate Insurance Company Usage-based insurance cost determination system and method
WO2006088916A2 (en) 2005-02-14 2006-08-24 Regents Of The University Of Minnesota Vehicle position system using passive roadway tags
US20060184295A1 (en) 2005-02-17 2006-08-17 Steve Hawkins On-board datalogger apparatus and service methods for use with vehicles
JP4650028B2 (en) 2005-03-02 2011-03-16 株式会社デンソー Driving evaluation device and driving evaluation system
JP2006252138A (en) 2005-03-10 2006-09-21 Omron Corp Apparatus for photographing driver and apparatus for monitoring driver
US20060212195A1 (en) 2005-03-15 2006-09-21 Veith Gregory W Vehicle data recorder and telematic device
US20060229777A1 (en) 2005-04-12 2006-10-12 Hudson Michael D System and methods of performing real-time on-board automotive telemetry analysis and reporting
JP4735310B2 (en) 2005-04-15 2011-07-27 株式会社デンソー Driving support device
WO2006121986A2 (en) 2005-05-06 2006-11-16 Facet Technology Corp. Network-based navigation system having virtual drive-thru advertisements integrated with actual imagery from along a physical route
US7835834B2 (en) 2005-05-16 2010-11-16 Delphi Technologies, Inc. Method of mitigating driver distraction
CA2609806A1 (en) 2005-06-01 2006-12-07 Innosurance, Inc. Motor vehicle operating data collection and analysis
US7327238B2 (en) 2005-06-06 2008-02-05 International Business Machines Corporation Method, system, and computer program product for determining and reporting tailgating incidents
AU2006256372B2 (en) 2005-06-09 2009-10-22 Greenroad Driving Technologies Ltd. System and method for displaying a driving profile
DE102005026479B4 (en) 2005-06-09 2017-04-20 Daimler Ag Method for inattention recognition as a function of at least one driver-individual parameter
US7693612B2 (en) 2005-06-23 2010-04-06 International Business Machines Corporation Method and system for updating code embedded in a vehicle
EP2436566B1 (en) 2005-07-11 2013-04-03 Volvo Technology Corporation Methods and arrangement for performing driver identity verification
EP1921984A2 (en) 2005-08-09 2008-05-21 Icap Technologies, Inc. Device and method relating to the emotional state of a person
WO2007022426A2 (en) 2005-08-18 2007-02-22 Environmental Systems Products Holdings Inc. System and method for testing the integrity of a vehicle testing/diagnostic system
JP2007069719A (en) 2005-09-06 2007-03-22 Honda Access Corp Data recording device for vehicle
US20070088469A1 (en) 2005-10-04 2007-04-19 Oshkosh Truck Corporation Vehicle control system and method
US20130332343A1 (en) 2005-10-06 2013-12-12 C-Sam, Inc. Multi-tiered, secure mobile transactions ecosystem enabling platform comprising a personalization tier, a service tier, and an enabling tier
WO2007047414A2 (en) 2005-10-12 2007-04-26 The Penn State Research Foundation Vigilance monitoring technique for vehicle operators
US8005467B2 (en) 2005-10-14 2011-08-23 General Motors Llc Method and system for providing a telematics readiness mode
US7733224B2 (en) 2006-06-30 2010-06-08 Bao Tran Mesh network personal emergency response appliance
DE112006003044T5 (en) 2005-10-21 2008-10-23 Deere & Company, Moline Versatile robot control module
US7920944B2 (en) 2005-10-21 2011-04-05 General Motors Llc Vehicle diagnostic test and reporting method
JP4971625B2 (en) 2005-11-14 2012-07-11 富士通テン株式会社 Driving support device and driving information calculation system
JP2007145200A (en) 2005-11-28 2007-06-14 Fujitsu Ten Ltd Authentication device for vehicle and authentication method for vehicle
US10878646B2 (en) 2005-12-08 2020-12-29 Smartdrive Systems, Inc. Vehicle event recorder systems
US20070132773A1 (en) 2005-12-08 2007-06-14 Smartdrive Systems Inc Multi-stage memory buffer and automatic transfers in vehicle event recording systems
EP1960983B1 (en) 2005-12-15 2012-08-29 International Business Machines Corporation Method, system and program for auditing vehicle speed compliance to an upcoming speed limit
DE102005062019A1 (en) 2005-12-22 2007-06-28 Robert Bosch Gmbh Messages e.g. traffic messages, coding method for describing e.g. traffic congestion in road network, involves including supplementary messages in contents of messages, where supplementary messages contain supplementing message contents
US9459622B2 (en) * 2007-01-12 2016-10-04 Legalforce, Inc. Driverless vehicle commerce network and community
US7423540B2 (en) 2005-12-23 2008-09-09 Delphi Technologies, Inc. Method of detecting vehicle-operator state
US20140172727A1 (en) * 2005-12-23 2014-06-19 Raj V. Abhyanker Short-term automobile rentals in a geo-spatial environment
US20070159354A1 (en) 2006-01-09 2007-07-12 Outland Research, Llc Intelligent emergency vehicle alert system and user interface
US8125530B2 (en) 2006-01-13 2012-02-28 Nec Corporation Information recording system, information recording device, information recording method, and information collecting program
US7516010B1 (en) 2006-01-27 2009-04-07 Navteg North America, Llc Method of operating a navigation system to provide parking availability information
KR20080106244A (en) 2006-02-13 2008-12-04 올 프로텍트 엘엘씨 Method and system for controlling a vehicle given to a third party
DE102006006850B4 (en) 2006-02-15 2022-12-29 Bayerische Motoren Werke Aktiengesellschaft Method of aligning a pivotable vehicle sensor
US20070208498A1 (en) 2006-03-03 2007-09-06 Inrix, Inc. Displaying road traffic condition information and user controls
GB0605069D0 (en) 2006-03-14 2006-04-26 Airmax Group Plc Method and system for driver style monitoring and analysing
US8996240B2 (en) 2006-03-16 2015-03-31 Smartdrive Systems, Inc. Vehicle event recorders with integrated web server
US8050863B2 (en) * 2006-03-16 2011-11-01 Gray & Company, Inc. Navigation and control system for autonomous vehicles
US9201842B2 (en) 2006-03-16 2015-12-01 Smartdrive Systems, Inc. Vehicle event recorder systems and networks having integrated cellular wireless communications systems
EP2008257A2 (en) 2006-03-30 2008-12-31 Asher S. Saban Protecting children and passengers with respect to a vehicle
US20080106390A1 (en) 2006-04-05 2008-05-08 White Steven C Vehicle power inhibiter
US8314708B2 (en) 2006-05-08 2012-11-20 Drivecam, Inc. System and method for reducing driving risk with foresight
JP4965162B2 (en) 2006-05-10 2012-07-04 トヨタ自動車株式会社 Arrhythmia monitoring device for vehicles
US9635534B2 (en) 2006-05-16 2017-04-25 RedSky Technologies, Inc. Method and system for an emergency location information service (E-LIS) from automated vehicles
US8095394B2 (en) 2006-05-18 2012-01-10 Progressive Casualty Insurance Company Rich claim reporting system
US20080294690A1 (en) 2007-05-22 2008-11-27 Mcclellan Scott System and Method for Automatically Registering a Vehicle Monitoring Device
US20080258890A1 (en) 2006-05-22 2008-10-23 Todd Follmer System and Method for Remotely Deactivating a Vehicle
US7498954B2 (en) 2006-05-31 2009-03-03 International Business Machines Corporation Cooperative parking
US20070282638A1 (en) 2006-06-04 2007-12-06 Martin Surovy Route based method for determining cost of automobile insurance
US7698086B2 (en) 2006-06-08 2010-04-13 Injury Sciences Llc Method and apparatus for obtaining and using event data recorder triage data
JP5036814B2 (en) 2006-06-11 2012-09-26 ボルボ テクノロジー コーポレイション Method and apparatus for determination and analysis of places of visual interest
US8947531B2 (en) * 2006-06-19 2015-02-03 Oshkosh Corporation Vehicle diagnostics based on information communicated between vehicles
US8139109B2 (en) 2006-06-19 2012-03-20 Oshkosh Corporation Vision system for an autonomous vehicle
US20130164715A1 (en) 2011-12-24 2013-06-27 Zonar Systems, Inc. Using social networking to improve driver performance based on industry sharing of driver performance data
US7813888B2 (en) 2006-07-24 2010-10-12 The Boeing Company Autonomous vehicle rapid development testbed systems and methods
US20080027761A1 (en) 2006-07-25 2008-01-31 Avraham Bracha System and method for verifying driver's insurance coverage
US7571682B2 (en) 2006-08-07 2009-08-11 Bianco Archangel J Safe correlator system for automatic car wash
US7570158B2 (en) 2006-08-17 2009-08-04 At&T Intellectual Property I, L.P. Collaborative incident media recording system and related methods
US7609150B2 (en) 2006-08-18 2009-10-27 Motorola, Inc. User adaptive vehicle hazard warning apparatuses and method
US8781442B1 (en) * 2006-09-08 2014-07-15 Hti Ip, Llc Personal assistance safety systems and methods
US20080064014A1 (en) 2006-09-12 2008-03-13 Drivingmba Llc Simulation-based novice driver instruction system and method
ES2345782T3 (en) * 2006-09-15 2010-10-01 Saab Ab PROVISION AND PROCEDURE TO GENERATE INPUT INFORMATION FOR A SIMULATION DEVICE.
US20080082372A1 (en) 2006-09-29 2008-04-03 Burch Leon A Driving simulator and method of evaluation of driver competency
US8531521B2 (en) 2006-10-06 2013-09-10 Sightlogix, Inc. Methods and apparatus related to improved surveillance using a smart camera
US8255026B1 (en) * 2006-10-12 2012-08-28 Masimo Corporation, Inc. Patient monitor capable of monitoring the quality of attached probes and accessories
JP4840069B2 (en) 2006-10-12 2011-12-21 アイシン・エィ・ダブリュ株式会社 Navigation system
US20080114530A1 (en) 2006-10-27 2008-05-15 Petrisor Gregory C Thin client intelligent transportation system and method for use therein
US8989959B2 (en) * 2006-11-07 2015-03-24 Smartdrive Systems, Inc. Vehicle operator performance history recording, scoring and reporting systems
US8868288B2 (en) 2006-11-09 2014-10-21 Smartdrive Systems, Inc. Vehicle exception event management systems
US8532862B2 (en) 2006-11-29 2013-09-10 Ryan A. Neff Driverless vehicle
EP2110797B1 (en) 2006-12-05 2015-10-07 Fujitsu Limited Traffic situation display method, traffic situation display system, vehicle-mounted device, and computer program
US20080147267A1 (en) 2006-12-13 2008-06-19 Smartdrive Systems Inc. Methods of Discretizing data captured at event data recorders
US8139820B2 (en) 2006-12-13 2012-03-20 Smartdrive Systems Inc. Discretization facilities for vehicle event data recorders
US20080143497A1 (en) 2006-12-15 2008-06-19 General Motors Corporation Vehicle Emergency Communication Mode Method and Apparatus
US9302678B2 (en) 2006-12-29 2016-04-05 Robotic Research, Llc Robotic driving system
US7792328B2 (en) 2007-01-12 2010-09-07 International Business Machines Corporation Warning a vehicle operator of unsafe operation behavior based on a 3D captured image stream
US7692552B2 (en) 2007-01-23 2010-04-06 International Business Machines Corporation Method and system for improving driver safety and situational awareness
US8078334B2 (en) 2007-01-23 2011-12-13 Alan Goodrich Unobtrusive system and method for monitoring the physiological condition of a target user of a vehicle
JP4400624B2 (en) 2007-01-24 2010-01-20 トヨタ自動車株式会社 Dozing prevention device and method
US20080180237A1 (en) * 2007-01-30 2008-07-31 Fayyad Salem A Vehicle emergency communication device and a method for transmitting emergency textual data utilizing the vehicle emergency communication device
WO2008097499A1 (en) 2007-02-02 2008-08-14 Hartford Fire Insurance Company Safety evaluation and feedback system and method
JP2008204286A (en) * 2007-02-21 2008-09-04 Communication Design:Kk Motivation management device and motivation management system
JP5056067B2 (en) 2007-02-26 2012-10-24 株式会社デンソー Dozing alarm device
US8123686B2 (en) 2007-03-01 2012-02-28 Abbott Diabetes Care Inc. Method and apparatus for providing rolling data in communication systems
JP4984974B2 (en) 2007-03-02 2012-07-25 富士通株式会社 Driving support system and in-vehicle device
EP1967931A3 (en) 2007-03-06 2013-10-30 Yamaha Hatsudoki Kabushiki Kaisha Vehicle
JP4720770B2 (en) 2007-04-02 2011-07-13 トヨタ自動車株式会社 Information recording system for vehicles
US20080255887A1 (en) 2007-04-10 2008-10-16 Autoonline Gmbh Informationssysteme Method and system for processing an insurance claim for a damaged vehicle
US8117049B2 (en) 2007-04-10 2012-02-14 Hti Ip, Llc Methods, systems, and apparatuses for determining driver behavior
US7853375B2 (en) 2007-04-10 2010-12-14 Maurice Tuff Vehicle monitor
US20080312969A1 (en) 2007-04-20 2008-12-18 Richard Raines System and method for insurance underwriting and rating
US20080258885A1 (en) 2007-04-21 2008-10-23 Synectic Systems Group Limited System and method for recording environmental data in vehicles
US8239092B2 (en) 2007-05-08 2012-08-07 Smartdrive Systems Inc. Distributed vehicle event recorder systems having a portable memory data transfer system
US10096038B2 (en) 2007-05-10 2018-10-09 Allstate Insurance Company Road segment safety rating system
US9932033B2 (en) 2007-05-10 2018-04-03 Allstate Insurance Company Route risk mitigation
US10157422B2 (en) * 2007-05-10 2018-12-18 Allstate Insurance Company Road segment safety rating
KR100778059B1 (en) 2007-05-22 2007-11-21 (주)텔릭스타 Apparatus and system blowing out dozing off motorways using facial recognition technology
WO2008151103A1 (en) 2007-05-31 2008-12-11 Hti Ip, Llc Methods, systems, and apparatuses for consumer telematics
JP4560739B2 (en) 2007-06-29 2010-10-13 アイシン・エィ・ダブリュ株式会社 Own vehicle position recognition device and own vehicle position recognition program
US7925423B2 (en) 2007-08-31 2011-04-12 Embarq Holdings Company, Llc System and method for traffic condition detection
US20090069953A1 (en) 2007-09-06 2009-03-12 University Of Alabama Electronic control system and associated methodology of dynamically conforming a vehicle operation
JP5036460B2 (en) * 2007-09-06 2012-09-26 トヨタ自動車株式会社 Vehicle travel control device
WO2009038797A2 (en) 2007-09-20 2009-03-26 Evolution Robotics Robotic game systems and methods
US8180655B1 (en) 2007-09-24 2012-05-15 United Services Automobile Association (Usaa) Systems and methods for processing vehicle or driver performance data
US8566126B1 (en) 2007-09-24 2013-10-22 United Services Automobile Association Systems and methods for processing vehicle or driver performance data
US7812740B2 (en) 2007-09-27 2010-10-12 Verizon Patent And Licensing Inc. Systems, devices, and methods for providing alert tones
US7719431B2 (en) 2007-10-05 2010-05-18 Gm Global Technology Operations, Inc. Systems, methods and computer products for drowsy driver detection and response
US20090106135A1 (en) 2007-10-19 2009-04-23 Robert James Steiger Home warranty method and system
US20090132294A1 (en) 2007-11-15 2009-05-21 Haines Samuel H Method for ranking driver's relative risk based on reported driving incidents
US20090140887A1 (en) 2007-11-29 2009-06-04 Breed David S Mapping Techniques Using Probe Vehicles
US7856294B2 (en) * 2007-12-14 2010-12-21 Sra International, Inc. Intelligent system and method for spacecraft autonomous operations
US8793048B2 (en) * 2007-12-26 2014-07-29 Hewlett-Packard Development Company, L.P. Apparatus and method for analyzing multiple fault occurrence of multiple-state device
US8031062B2 (en) 2008-01-04 2011-10-04 Smith Alexander E Method and apparatus to improve vehicle situational awareness at intersections
US8228359B2 (en) 2008-01-08 2012-07-24 International Business Machines Corporation Device, method and computer program product for responding to media conference deficiencies
US9665910B2 (en) 2008-02-20 2017-05-30 Hartford Fire Insurance Company System and method for providing customized safety feedback
US9026304B2 (en) 2008-04-07 2015-05-05 United Parcel Service Of America, Inc. Vehicle maintenance systems and methods
US8019629B1 (en) 2008-04-07 2011-09-13 United Services Automobile Association (Usaa) Systems and methods for automobile accident claims initiation
US20090265193A1 (en) 2008-04-17 2009-10-22 Collins Dean Methods and systems for automated property insurance inspection
US8605947B2 (en) 2008-04-24 2013-12-10 GM Global Technology Operations LLC Method for detecting a clear path of travel for a vehicle enhanced by object detection
JP4547721B2 (en) 2008-05-21 2010-09-22 株式会社デンソー Automotive information provision system
US20090300065A1 (en) 2008-05-30 2009-12-03 Birchall James T Computer system and methods for improving identification of subrogation opportunities
KR101141874B1 (en) 2008-06-04 2012-05-08 주식회사 만도 Apparatus, Method for Dectecting Critical Areas and Pedestrian Detection Apparatus Using Same
US8068983B2 (en) * 2008-06-11 2011-11-29 The Boeing Company Virtual environment systems and methods
US8160811B2 (en) * 2008-06-26 2012-04-17 Toyota Motor Engineering & Manufacturing North America, Inc. Method and system to estimate driving risk based on a hierarchical index of driving
US20100004995A1 (en) 2008-07-07 2010-01-07 Google Inc. Claiming Real Estate in Panoramic or 3D Mapping Environments for Advertising
US8346468B2 (en) 2008-07-08 2013-01-01 Sky-Trax Incorporated Method and apparatus for collision avoidance
GB2474405A (en) 2008-07-31 2011-04-13 Choicepoint Services Inc Systems & methods of calculating and presenting automobile driving risks
KR101040118B1 (en) 2008-08-04 2011-06-09 한국전자통신연구원 Apparatus for reconstructing traffic accident and control method thereof
US7973674B2 (en) 2008-08-20 2011-07-05 International Business Machines Corporation Vehicle-to-vehicle traffic queue information communication system and method
JP4602444B2 (en) 2008-09-03 2010-12-22 株式会社日立製作所 Driver driving skill support apparatus and driver driving skill support method
US8140359B2 (en) 2008-09-11 2012-03-20 F3M3 Companies, Inc, System and method for determining an objective driver score
US8483910B2 (en) 2008-09-18 2013-07-09 Trw Automotive U.S. Llc Method of controlling a vehicle steering apparatus
FR2936631B1 (en) 2008-09-29 2011-03-25 Act Concepts METHOD AND DEVICE FOR AUTHENTICATING TRANSMITTED DATA RELATING TO THE USE OF A VEHICLE AND / OR BEHAVIOR OF ITS DRIVER
JP2010086265A (en) 2008-09-30 2010-04-15 Fujitsu Ltd Receiver, data display method, and movement support system
KR101502012B1 (en) 2008-10-06 2015-03-12 엘지전자 주식회사 Telematics terminal and method for notifying emrergency condition using the same
US20100106346A1 (en) * 2008-10-23 2010-04-29 Honeywell International Inc. Method and system for managing flight plan data
US8027853B1 (en) 2008-10-23 2011-09-27 United States Automobile Associates (USAA) Systems and methods for self-service vehicle risk adjustment
US8126642B2 (en) * 2008-10-24 2012-02-28 Gray & Company, Inc. Control and systems for autonomously driven vehicles
EP2347400B1 (en) 2008-11-07 2014-03-12 Volvo Lastvagnar AB Method and system for combining sensor data
US20100131300A1 (en) * 2008-11-26 2010-05-27 Fred Collopy Visible insurance
US8473143B2 (en) 2008-12-02 2013-06-25 Caterpillar Inc. System and method for accident logging in an automated machine
JP2010164944A (en) 2008-12-16 2010-07-29 Olympus Corp Projection optical system and visual display apparatus using the same
TWI399565B (en) 2008-12-31 2013-06-21 Univ Nat Taiwan Pressure sensing based localization and tracking system
US20100198491A1 (en) 2009-02-05 2010-08-05 Paccar Inc Autonomic vehicle safety system
US8188887B2 (en) 2009-02-13 2012-05-29 Inthinc Technology Solutions, Inc. System and method for alerting drivers to road conditions
US8451105B2 (en) 2009-02-25 2013-05-28 James Holland McNay Security and driver identification system
US8054168B2 (en) 2009-02-27 2011-11-08 General Motors Llc System and method for estimating an emergency level of a vehicular accident
US9674458B2 (en) 2009-06-03 2017-06-06 Flir Systems, Inc. Smart surveillance camera systems and methods
US8332242B1 (en) 2009-03-16 2012-12-11 United Services Automobile Association (Usaa) Systems and methods for real-time driving risk prediction and route recommendation
US9727920B1 (en) 2009-03-16 2017-08-08 United Services Automobile Association (Usaa) Insurance policy management using telematics
US8040247B2 (en) 2009-03-23 2011-10-18 Toyota Motor Engineering & Manufacturing North America, Inc. System for rapid detection of drowsiness in a machine operator
US8108655B2 (en) * 2009-03-24 2012-01-31 International Business Machines Corporation Selecting fixed-point instructions to issue on load-store unit
US8395529B2 (en) 2009-04-02 2013-03-12 GM Global Technology Operations LLC Traffic infrastructure indicator on head-up display
US20100257477A1 (en) 2009-04-03 2010-10-07 Certusview Technologies, Llc Methods, apparatus, and systems for documenting and reporting events via geo-referenced electronic drawings
US8352112B2 (en) 2009-04-06 2013-01-08 GM Global Technology Operations LLC Autonomous vehicle management
WO2010117308A1 (en) 2009-04-07 2010-10-14 Volvo Technology Corporation Method and system to enhance traffic safety and efficiency for vehicles
DE102009018761A1 (en) 2009-04-27 2010-10-28 Bayerische Motoren Werke Aktiengesellschaft Process for updating software components
US20100286845A1 (en) 2009-05-11 2010-11-11 Andrew Karl Wilhelm Rekow Fail-safe system for autonomous vehicle
US20120135382A1 (en) 2009-05-12 2012-05-31 The Children's Hospital Of Philadelphia Individualized mastery-based driver training
US8751293B2 (en) 2009-05-14 2014-06-10 Microsoft Corporation Delivering contextual advertising to a vehicle
US20100299021A1 (en) 2009-05-21 2010-11-25 Reza Jalili System and Method for Recording Data Associated with Vehicle Activity and Operation
JP5216690B2 (en) 2009-06-01 2013-06-19 株式会社日立製作所 Robot management system, robot management terminal, robot management method and program
US8106769B1 (en) 2009-06-26 2012-01-31 United Services Automobile Association (Usaa) Systems and methods for automated house damage detection and reporting
WO2011008697A2 (en) 2009-07-13 2011-01-20 Michael Self Asynchronous voice and/or video communication system and method using wireless devices
US8427326B2 (en) 2009-07-30 2013-04-23 Meir Ben David Method and system for detecting the physiological onset of operator fatigue, drowsiness, or performance decrement
FR2948759B1 (en) 2009-07-31 2011-08-12 Movea METHOD FOR ESTIMATING THE ORIENTATION OF A SOLID IN MOTION
CA2754159C (en) 2009-08-11 2012-05-15 Certusview Technologies, Llc Systems and methods for complex event processing of vehicle-related information
US9412130B2 (en) 2009-08-19 2016-08-09 Allstate Insurance Company Assistance on the go
US9384491B1 (en) 2009-08-19 2016-07-05 Allstate Insurance Company Roadside assistance
US9070243B1 (en) 2009-08-19 2015-06-30 Allstate Insurance Company Assistance on the go
US9552726B2 (en) 2009-08-24 2017-01-24 Here Global B.V. Providing driving condition alerts using road attribute data
ES2561803T3 (en) 2009-08-31 2016-03-01 Accenture Global Services Limited Method implemented by computer to ensure the privacy of a user, computer program product, device
FR2953029B1 (en) 2009-11-25 2011-11-18 Draka Comteq France MULTIMODE OPTICAL FIBER WITH LARGE BANDWIDTH WITH AN OPTIMIZED HEAT-SLEEVE INTERFACE
JP4816780B2 (en) 2009-09-11 2011-11-16 株式会社デンソー On-vehicle charge / discharge control device and partial control device included therein
CN102640380B (en) 2009-09-28 2015-06-17 电力消防栓有限责任公司 Method and system for charging electric vehicles
US8645005B2 (en) 2009-10-01 2014-02-04 Alfred B. Elkins Multipurpose modular airship systems and methods
US20110087505A1 (en) * 2009-10-14 2011-04-14 Summit Mobile Solutions, Inc. Method and system for damage reporting and repair
US8253589B2 (en) 2009-10-20 2012-08-28 GM Global Technology Operations LLC Vehicle to entity communication
US8604920B2 (en) 2009-10-20 2013-12-10 Cartasite, Inc. Systems and methods for vehicle performance analysis and presentation
US9082308B2 (en) 2009-10-20 2015-07-14 Cartasite Inc. Driver performance analysis and consequence
WO2011057217A2 (en) 2009-11-06 2011-05-12 University Of Utah Research Foundation Method for gathering, processing, and analyzing data to determine crash risk associated with driving behavior
US8339268B2 (en) 2009-11-10 2012-12-25 GM Global Technology Operations LLC Driver configurable drowsiness prevention
US8423239B2 (en) 2009-11-23 2013-04-16 Hti Ip, L.L.C. Method and system for adjusting a charge related to use of a vehicle during a period based on operational performance data
US8386168B2 (en) 2009-11-24 2013-02-26 Verizon Patent And Licensing Inc. Traffic data collection in a navigational system
US20110128161A1 (en) 2009-11-30 2011-06-02 Gm Global Technology Operations, Inc. Vehicular warning device for pedestrians
JP5045796B2 (en) 2009-12-03 2012-10-10 株式会社デンソー Vehicle approach warning system, portable warning terminal, and in-vehicle communication device
DE102009056786A1 (en) 2009-12-03 2011-06-09 Continental Automotive Gmbh Mobile interface and system for controlling vehicle functions
US20120056758A1 (en) 2009-12-03 2012-03-08 Delphi Technologies, Inc. Vehicle parking spot locator system and method using connected vehicles
US20110137684A1 (en) 2009-12-08 2011-06-09 Peak David F System and method for generating telematics-based customer classifications
US8452482B2 (en) 2009-12-10 2013-05-28 GM Global Technology Operations LLC Self testing systems and methods
US8742987B2 (en) 2009-12-10 2014-06-03 GM Global Technology Operations LLC Lean V2X security processing strategy using kinematics information of vehicles
JP5269755B2 (en) 2009-12-10 2013-08-21 株式会社日立製作所 Cross-person support vehicle system and cross-person support method
US8635091B2 (en) 2009-12-17 2014-01-21 Hartford Fire Insurance Company Systems and methods for linking vehicles to telematics-enabled portable devices
US20110161119A1 (en) 2009-12-24 2011-06-30 The Travelers Companies, Inc. Risk assessment and control, insurance premium determinations, and other applications using busyness
US20110304465A1 (en) 2009-12-30 2011-12-15 Boult Terrance E System and method for driver reaction impairment vehicle exclusion via systematic measurement for assurance of reaction time
US9558520B2 (en) 2009-12-31 2017-01-31 Hartford Fire Insurance Company System and method for geocoded insurance processing using mobile devices
EP2525336B1 (en) 2010-01-12 2021-11-24 Toyota Jidosha Kabushiki Kaisha Collision position predicting device
US8384534B2 (en) 2010-01-14 2013-02-26 Toyota Motor Engineering & Manufacturing North America, Inc. Combining driver and environment sensing for vehicular safety systems
DE102010001006A1 (en) * 2010-01-19 2011-07-21 Robert Bosch GmbH, 70469 Car accident information providing method for insurance company, involves information about accident is transmitted from sensor to data processing unit of driverless car by communication module of car over network connection
US20110190972A1 (en) 2010-02-02 2011-08-04 Gm Global Technology Operations, Inc. Grid unlock
US9254781B2 (en) 2010-02-02 2016-02-09 Craig David Applebaum Emergency vehicle warning device and system
US20110196571A1 (en) 2010-02-09 2011-08-11 At&T Mobility Ii Llc System And Method For The Collection And Monitoring Of Vehicle Data
US20120004933A1 (en) 2010-02-09 2012-01-05 At&T Mobility Ii Llc System And Method For The Collection And Monitoring Of Vehicle Data
US20120010906A1 (en) 2010-02-09 2012-01-12 At&T Mobility Ii Llc System And Method For The Collection And Monitoring Of Vehicle Data
US9043041B2 (en) 2010-02-12 2015-05-26 Webtech Wireless Inc. Monitoring aggressive driving operation of a mobile asset
US20110251751A1 (en) 2010-03-11 2011-10-13 Lee Knight Motorized equipment tracking and monitoring apparatus, system and method
CN102469948B (en) 2010-03-12 2015-06-03 塔塔咨询服务有限公司 A system for vehicle security, personalization and cardiac activity monitoring of a driver
US8618922B2 (en) 2010-03-30 2013-12-31 GM Global Technology Operations LLC Method and system for ensuring operation of limited-ability autonomous driving vehicles
JP5595298B2 (en) * 2010-04-06 2014-09-24 キヤノン株式会社 Solid-state imaging device and imaging system
CN102859457B (en) 2010-04-26 2015-11-25 株式会社日立制作所 Time series data diagnosis compression method
US8548646B1 (en) 2010-05-04 2013-10-01 Clearpath Robotics Inc. Distributed hardware architecture for unmanned vehicles
US20110279263A1 (en) 2010-05-13 2011-11-17 Ryan Scott Rodkey Event Detection
US20160086393A1 (en) 2010-05-17 2016-03-24 The Travelers Indemnity Company Customized vehicle monitoring privacy system
CA2799714C (en) 2010-05-17 2017-08-22 The Travelers Indemnity Company Monitoring customer-selected vehicle parameters
US20120101855A1 (en) 2010-05-17 2012-04-26 The Travelers Indemnity Company Monitoring client-selected vehicle parameters in accordance with client preferences
US8258978B2 (en) 2010-05-19 2012-09-04 Garmin Switzerland Gmbh Speed limit change notification
US8700353B2 (en) 2010-05-27 2014-04-15 Incheck Technologies, Inc. MEMS accelerometer device
US8744745B2 (en) 2010-06-08 2014-06-03 General Motors Llc Method of using vehicle location information with a wireless mobile device
EP2405132B1 (en) 2010-07-09 2016-04-27 Siemens Aktiengesellschaft Wind turbine, drive train assembly, wind turbine nacelle system, methods for converting rotational energy and methods for building a nacelle and for re-equipping a wind turbine
JP2012022837A (en) 2010-07-13 2012-02-02 Canon Inc Image display unit
US9418554B2 (en) 2014-08-07 2016-08-16 Verizon Patent And Licensing Inc. Method and system for determining road conditions based on driver data
JP5293699B2 (en) 2010-08-11 2013-09-18 トヨタ自動車株式会社 Vehicle control device
KR101605453B1 (en) 2010-08-25 2016-04-01 네이버 주식회사 Internet telematics service providing system and internet telematics service providing method for providing mileage-related driving information
GB2483251A (en) 2010-09-01 2012-03-07 Ricardo Uk Ltd Driver feedback system and method
US8968197B2 (en) 2010-09-03 2015-03-03 International Business Machines Corporation Directing a user to a medical resource
US20120066007A1 (en) 2010-09-14 2012-03-15 Ferrick David P System and Method for Tracking and Sharing Driving Metrics with a Plurality of Insurance Carriers
WO2012040392A2 (en) 2010-09-21 2012-03-29 Cellepathy Ltd. System and method for sensor-based determination of user role, location, and/or state of one of more in-vehicle mobile devices and enforcement of usage thereof
US20120083668A1 (en) 2010-09-30 2012-04-05 Anantha Pradeep Systems and methods to modify a characteristic of a user device based on a neurological and/or physiological measurement
US9014911B2 (en) 2011-11-16 2015-04-21 Flextronics Ap, Llc Street side sensors
US8509982B2 (en) 2010-10-05 2013-08-13 Google Inc. Zone driving
KR101207903B1 (en) 2010-10-11 2012-12-04 국방과학연구소 Apparatus and method for providing the obstacle information of autonomous mobile vehicle
US8447231B2 (en) 2010-10-29 2013-05-21 GM Global Technology Operations LLC Intelligent telematics information dissemination using delegation, fetch, and share algorithms
US20120106653A1 (en) 2010-11-03 2012-05-03 Broadcom Corporation Multimedia processing within a vehicular communication network
US20120108909A1 (en) 2010-11-03 2012-05-03 HeadRehab, LLC Assessment and Rehabilitation of Cognitive and Motor Functions Using Virtual Reality
KR20120058230A (en) 2010-11-29 2012-06-07 한국전자통신연구원 Safe operation apparatus for mobile objects and method thereof
US9507413B2 (en) 2010-12-03 2016-11-29 Continental Automotive Systems, Inc. Tailoring vehicle human machine interface
US20120143630A1 (en) 2010-12-07 2012-06-07 International Business Machines Corporation Third party verification of insurable incident claim submission
EP3255613B1 (en) * 2010-12-15 2022-09-21 Auto Telematics Ltd Method and system for logging vehicle behaviour
US9026134B2 (en) 2011-01-03 2015-05-05 Qualcomm Incorporated Target positioning within a mobile structure
US8863256B1 (en) * 2011-01-14 2014-10-14 Cisco Technology, Inc. System and method for enabling secure transactions using flexible identity management in a vehicular environment
EP3514752A1 (en) 2011-01-17 2019-07-24 Imetrik Technologies Inc. Computer-implemented method and system for reporting a confidence score in relation to a vehicle equipped with a wireless-enabled usage reporting device
US9086297B2 (en) 2011-01-20 2015-07-21 Telenav, Inc. Navigation system having maneuver attempt training mechanism and method of operation thereof
US9081409B2 (en) 2011-01-21 2015-07-14 The United States Of America As Represented By The Secretary Of The Navy Event detection control system for operating a remote sensor or projectile system
US8928495B2 (en) 2011-01-24 2015-01-06 Lexisnexis Risk Solutions Inc. Systems and methods for telematics monitoring and communications
KR20120086140A (en) * 2011-01-25 2012-08-02 한국전자통신연구원 Mobile and apparatus for providing auto valet parking service and method thereof
US20120190001A1 (en) 2011-01-25 2012-07-26 Hemisphere Centre for Mental Health & Wellness Inc. Automated cognitive testing methods and applications therefor
WO2012103306A2 (en) 2011-01-27 2012-08-02 Berkeley Telematics Inc. Determining cost for auto insurance
EP2484573B1 (en) 2011-02-08 2017-12-13 Volvo Car Corporation Method for reducing the risk of a collision between a vehicle and a first external object
JP5729861B2 (en) 2011-02-08 2015-06-03 本田技研工業株式会社 Vehicle driving support device
US8902054B2 (en) 2011-02-10 2014-12-02 Sitting Man, Llc Methods, systems, and computer program products for managing operation of a portable electronic device
US8698639B2 (en) 2011-02-18 2014-04-15 Honda Motor Co., Ltd. System and method for responding to driver behavior
US8731736B2 (en) 2011-02-22 2014-05-20 Honda Motor Co., Ltd. System and method for reducing driving skill atrophy
US9542846B2 (en) 2011-02-28 2017-01-10 GM Global Technology Operations LLC Redundant lane sensing systems for fault-tolerant vehicular lateral controller
US9928524B2 (en) 2011-03-14 2018-03-27 GM Global Technology Operations LLC Learning driver demographics from vehicle trace data
US8725311B1 (en) 2011-03-14 2014-05-13 American Vehicular Sciences, LLC Driver health and fatigue monitoring system and method
US8593277B2 (en) 2011-03-17 2013-11-26 Kaarya, LLC. System and method for proximity detection
EP2500887B1 (en) 2011-03-17 2020-09-09 Harman Becker Automotive Systems GmbH Description of a Road Segment Using ISO 17572-3
US8880289B2 (en) 2011-03-17 2014-11-04 Toyota Motor Engineering & Manufacturing North America, Inc. Vehicle maneuver application interface
US20120303222A1 (en) 2011-03-23 2012-11-29 Tk Holding Inc. Driver assistance system
JP2012222435A (en) 2011-04-05 2012-11-12 Denso Corp Portable terminal, vehicle-mounted device, communication system, program for portable terminal, and control method
US20120256769A1 (en) 2011-04-07 2012-10-11 GM Global Technology Operations LLC System and method for real-time detection of an emergency situation occuring in a vehicle
DE102011016772A1 (en) 2011-04-12 2012-10-18 Daimler Ag Method and device for monitoring at least one vehicle occupant and method for operating at least one assistance device
US8849483B2 (en) 2011-04-13 2014-09-30 California Institute Of Technology Target trailing with safe navigation with colregs for maritime autonomous surface vehicles
US8909426B2 (en) 2011-04-19 2014-12-09 Ford Global Technologies Trailer path curvature control for trailer backup assist
US20120271500A1 (en) 2011-04-20 2012-10-25 GM Global Technology Operations LLC System and method for enabling a driver to input a vehicle control instruction into an autonomous vehicle controller
US9581997B1 (en) 2011-04-22 2017-02-28 Angel A. Penilla Method and system for cloud-based communication for automatic driverless movement
US9424606B2 (en) 2011-04-28 2016-08-23 Allstate Insurance Company Enhanced claims settlement
WO2012150591A2 (en) 2011-05-03 2012-11-08 Alon Atsmon Automatic content analysis method and system
US8935071B2 (en) * 2011-05-05 2015-01-13 GM Global Technology Operations LLC Optimal fusion of electric park brake and hydraulic brake sub-system functions to control vehicle direction
US8670903B2 (en) * 2011-05-05 2014-03-11 GM Global Technology Operations LLC Lane centering fail-safe control using differential braking
US20120289819A1 (en) 2011-05-09 2012-11-15 Allergan, Inc. Implant detector
US20120286974A1 (en) 2011-05-11 2012-11-15 Siemens Corporation Hit and Run Prevention and Documentation System for Vehicles
US9229450B2 (en) 2011-05-31 2016-01-05 Hitachi, Ltd. Autonomous movement system
US8466807B2 (en) 2011-06-01 2013-06-18 GM Global Technology Operations LLC Fast collision detection technique for connected autonomous and manual vehicles
US20120316455A1 (en) 2011-06-10 2012-12-13 Aliphcom Wearable device and platform for sensory input
US20110307188A1 (en) 2011-06-29 2011-12-15 State Farm Insurance Systems and methods for providing driver feedback using a handheld mobile device
US20130006674A1 (en) 2011-06-29 2013-01-03 State Farm Insurance Systems and Methods Using a Mobile Device to Collect Data for Insurance Premiums
KR20130004824A (en) 2011-07-04 2013-01-14 현대자동차주식회사 Vehicle control system
US8996226B1 (en) 2011-07-12 2015-03-31 Google Inc. Intersection completer
US8762044B2 (en) * 2011-07-13 2014-06-24 Dynamic Research, Inc. System and method for testing crash avoidance technologies
US9165470B2 (en) 2011-07-25 2015-10-20 GM Global Technology Operations LLC Autonomous convoying technique for vehicles
DE102011109564A1 (en) 2011-08-05 2013-02-07 Daimler Ag Method and device for monitoring at least one vehicle occupant and method for operating at least one assistance device
US20130038437A1 (en) 2011-08-08 2013-02-14 Panasonic Corporation System for task and notification handling in a connected car
US8554468B1 (en) 2011-08-12 2013-10-08 Brian Lee Bullock Systems and methods for driver performance assessment and improvement
DE102011110486A1 (en) 2011-08-17 2013-02-21 Daimler Ag Method and device for monitoring at least one vehicle occupant and method for operating at least one assistance device
US20130044008A1 (en) 2011-08-19 2013-02-21 Gpsi, Llc Enhanced emergency system using a hazard light device
US8538785B2 (en) 2011-08-19 2013-09-17 Hartford Fire Insurance Company System and method for computing and scoring the complexity of a vehicle trip using geo-spatial information
EP2751118B1 (en) 2011-08-29 2016-10-12 Bristol-Myers Squibb Company Spiro bicyclic diamine derivatives as hiv attachment inhibitors
EP2564765B1 (en) 2011-09-02 2017-12-13 Volvo Car Corporation System and method for improving a performance estimation of an operator of a vehicle
MX337513B (en) 2011-09-19 2016-03-09 Tata Consultancy Services Ltd A computing platform for development and deployment of sensor-driven vehicle telemetry applications and services.
US20130073193A1 (en) 2011-09-19 2013-03-21 Cambridge Silicon Radio Limited Collaborative traffic monitoring
JP2013091380A (en) * 2011-10-25 2013-05-16 Toyota Motor Corp Vehicle control system
US20160189544A1 (en) 2011-11-16 2016-06-30 Autoconnect Holdings Llc Method and system for vehicle data collection regarding traffic
US9235987B2 (en) 2011-11-17 2016-01-12 GM Global Technology Operations LLC System and method for closed-loop driver attention management
US9008853B2 (en) 2011-12-07 2015-04-14 GM Global Technology Operations LLC Vehicle operator identification and operator-configured services
US20130227409A1 (en) 2011-12-07 2013-08-29 Qualcomm Incorporated Integrating sensation functionalities into social networking services and applications
JP5832666B2 (en) 2011-12-20 2015-12-16 インテル・コーポレーション Augmented reality representation across multiple devices
CN103188647A (en) 2011-12-29 2013-07-03 北京网秦天下科技有限公司 Method and system for statistically analyzing and warning Internet surfing flow of mobile terminal
US8915738B2 (en) 2012-01-24 2014-12-23 Toyota Motor Engineering & Manufacturing North America, Inc. Driver quality assessment for driver education
US9650762B2 (en) 2012-01-24 2017-05-16 Harnischfeger Technologies, Inc. System and method for monitoring mining machine efficiency
US9381916B1 (en) * 2012-02-06 2016-07-05 Google Inc. System and method for predicting behaviors of detected objects through environment representation
KR101703144B1 (en) 2012-02-09 2017-02-06 한국전자통신연구원 Apparatus and method for autonomous driving
DE102012002695A1 (en) 2012-02-14 2013-08-14 Wabco Gmbh Method for determining an emergency braking situation of a vehicle
US20130218603A1 (en) 2012-02-21 2013-08-22 Elwha Llc Systems and methods for insurance based upon characteristics of a collision detection system
US20130218604A1 (en) 2012-02-21 2013-08-22 Elwha Llc Systems and methods for insurance based upon monitored characteristics of a collision detection system
US9299108B2 (en) 2012-02-24 2016-03-29 Tata Consultancy Services Limited Insurance claims processing
DE102012202914A1 (en) 2012-02-27 2013-08-29 Robert Bosch Gmbh Diagnostic method and diagnostic device for a vehicle component of a vehicle
DE102012101686A1 (en) * 2012-03-01 2013-09-05 Continental Teves Ag & Co. Ohg Method for a driver assistance system for the autonomous longitudinal and / or transverse control of a vehicle
US9429943B2 (en) 2012-03-05 2016-08-30 Florida A&M University Artificial intelligence valet systems and methods
US20140310075A1 (en) 2013-04-15 2014-10-16 Flextronics Ap, Llc Automatic Payment of Fees Based on Vehicle Location and User Detection
US20130245881A1 (en) 2012-03-14 2013-09-19 Christopher G. Scarbrough System and Method for Monitoring the Environment In and Around an Automobile
US20140306799A1 (en) 2013-04-15 2014-10-16 Flextronics Ap, Llc Vehicle Intruder Alert Detection and Indication
US8340902B1 (en) 2012-03-15 2012-12-25 Yan-Hong Chiang Remote vehicle management system by video radar
GB2500581B (en) 2012-03-23 2014-08-20 Jaguar Land Rover Ltd Method and system for controlling the output of information to a driver based on an estimated driver workload
US8847781B2 (en) 2012-03-28 2014-09-30 Sony Corporation Building management system with privacy-guarded assistance mechanism and method of operation thereof
DE102012007119A1 (en) 2012-04-05 2013-10-24 Audi Ag Method for operating a motor vehicle during and / or after a collision
US8718861B1 (en) 2012-04-11 2014-05-06 Google Inc. Determining when to drive autonomously
US8700251B1 (en) * 2012-04-13 2014-04-15 Google Inc. System and method for automatically detecting key behaviors by vehicles
JP5670949B2 (en) 2012-04-16 2015-02-18 日立建機株式会社 Operation management system
US9129532B2 (en) * 2012-04-24 2015-09-08 Zetta Research and Development LLC, ForC series Hybrid protocol transceiver for V2V communication
US20130278441A1 (en) 2012-04-24 2013-10-24 Zetta Research and Development, LLC - ForC Series Vehicle proxying
DE102012008858A1 (en) 2012-04-28 2012-11-08 Daimler Ag Method for performing autonomous parking process of motor vehicle e.g. passenger car, involves storing target position and/or last driven trajectory of vehicle in suitable device prior to start of autonomous vehicle parking operation
US8595037B1 (en) 2012-05-08 2013-11-26 Elwha Llc Systems and methods for insurance based on monitored characteristics of an autonomous drive mode selection system
US20130304514A1 (en) * 2012-05-08 2013-11-14 Elwha Llc Systems and methods for insurance based on monitored characteristics of an autonomous drive mode selection system
US8781669B1 (en) 2012-05-14 2014-07-15 Google Inc. Consideration of risks in active sensing for an autonomous vehicle
US9891709B2 (en) 2012-05-16 2018-02-13 Immersion Corporation Systems and methods for content- and context specific haptic effects using predefined haptic effects
US8880291B2 (en) 2012-05-17 2014-11-04 Harman International Industries, Inc. Methods and systems for preventing unauthorized vehicle operation using face recognition
US9037394B2 (en) 2012-05-22 2015-05-19 Hartford Fire Insurance Company System and method to determine an initial insurance policy benefit based on telematics data collected by a smartphone
US8768565B2 (en) 2012-05-23 2014-07-01 Enterprise Holdings, Inc. Rental/car-share vehicle access and management system and method
US10387960B2 (en) 2012-05-24 2019-08-20 State Farm Mutual Automobile Insurance Company System and method for real-time accident documentation and claim submission
US20130317786A1 (en) 2012-05-24 2013-11-28 Fluor Technologies Corporation Feature-based rapid structure modeling system
US8781721B2 (en) * 2012-06-06 2014-07-15 Google Inc. Obstacle evaluation technique
US8917182B2 (en) 2012-06-06 2014-12-23 Honda Motor Co., Ltd. System and method for detecting and preventing drowsiness
US9020876B2 (en) 2012-06-07 2015-04-28 International Business Machines Corporation On-demand suggestion for vehicle driving
US20130339062A1 (en) 2012-06-14 2013-12-19 Seth Brewer System and method for use of social networks to respond to insurance related events
US9425978B2 (en) 2012-06-27 2016-08-23 Ubiquiti Networks, Inc. Method and apparatus for configuring and controlling interfacing devices
US20140004734A1 (en) 2012-06-27 2014-01-02 Phan F. Hoang Insertion tool for memory modules
US20140002651A1 (en) 2012-06-30 2014-01-02 James Plante Vehicle Event Recorder Systems
US9558667B2 (en) 2012-07-09 2017-01-31 Elwha Llc Systems and methods for cooperative collision detection
US9165469B2 (en) 2012-07-09 2015-10-20 Elwha Llc Systems and methods for coordinating sensor operation for collision detection
GB2504080A (en) * 2012-07-16 2014-01-22 Bae Systems Plc Health impact assessment modelling to predict system health and consequential future capability changes in completion of objectives or mission
DE102012106522A1 (en) 2012-07-18 2014-01-23 Huf Hülsbeck & Fürst Gmbh & Co. Kg Method for authenticating a driver in a motor vehicle
US20140039934A1 (en) 2012-08-01 2014-02-06 Gabriel Ernesto RIVERA Insurance verification system (insvsys)
US9390628B2 (en) 2012-08-10 2016-07-12 Xrs Corporation Vehicle data and driver association for transportation management
US20140047371A1 (en) 2012-08-10 2014-02-13 Smartdrive Systems Inc. Vehicle Event Playback Apparatus and Methods
US8862321B2 (en) 2012-08-15 2014-10-14 GM Global Technology Operations LLC Directing vehicle into feasible region for autonomous and semi-autonomous parking
WO2014028010A2 (en) 2012-08-15 2014-02-20 Empire Technology Development Llc Estimating insurance risks and costs
US8510196B1 (en) 2012-08-16 2013-08-13 Allstate Insurance Company Feedback loop in mobile damage assessment and claims processing
DE102012017497B3 (en) 2012-08-17 2013-12-05 Audi Ag Traffic system for autonomous driving and method for determining a vehicle damage
DE102012214852B4 (en) 2012-08-21 2024-01-18 Robert Bosch Gmbh Method and device for selecting objects in the surroundings of a vehicle
US20140058761A1 (en) 2012-08-21 2014-02-27 Insurance Services Office, Inc. Apparatus and Method for Analyzing Driving Performance Data
US20140059066A1 (en) 2012-08-24 2014-02-27 EmoPulse, Inc. System and method for obtaining and using user physiological and emotional data
US9056395B1 (en) 2012-09-05 2015-06-16 Google Inc. Construction zone sign detection using light detection and ranging
US8996228B1 (en) 2012-09-05 2015-03-31 Google Inc. Construction zone object detection using light detection and ranging
KR102075110B1 (en) 2012-09-07 2020-02-10 주식회사 만도 Apparatus of identificating vehicle based vehicle-to-vehicle communication, and method of thereof
GB2506365B (en) 2012-09-26 2017-12-20 Masternaut Risk Solutions Ltd Vehicle incident detection
US9221396B1 (en) 2012-09-27 2015-12-29 Google Inc. Cross-validating sensors of an autonomous vehicle
US9665101B1 (en) * 2012-09-28 2017-05-30 Waymo Llc Methods and systems for transportation to destinations by a self-driving vehicle
CN104583888B (en) 2012-09-28 2016-12-21 株式会社日立制作所 Autonomous device and autonomous system
US9188985B1 (en) 2012-09-28 2015-11-17 Google Inc. Suggesting a route based on desired amount of driver interaction
US9156476B2 (en) 2012-10-02 2015-10-13 Trevor O'Neill System and method for remote control of unmanned vehicles
US20140095214A1 (en) 2012-10-03 2014-04-03 Robert E. Mathe Systems and methods for providing a driving performance platform
US9002719B2 (en) * 2012-10-08 2015-04-07 State Farm Mutual Automobile Insurance Company Device and method for building claim assessment
WO2014059084A1 (en) 2012-10-11 2014-04-17 Automatic Labs, Inc. Reputation system based on driving behavior
CN104885136B (en) 2012-10-12 2018-01-02 Newtrax控股有限公司 Context aware collision avoidance device and the anti-collision system for including the collision avoidance device
US9282436B2 (en) 2012-10-17 2016-03-08 Cellco Partnership Method and system for adaptive location determination for mobile device
US9560071B2 (en) 2012-10-17 2017-01-31 Tower-Sec Ltd. Device for detection and prevention of an attack on a vehicle
US9443207B2 (en) 2012-10-22 2016-09-13 The Boeing Company Water area management system
US20140114691A1 (en) 2012-10-23 2014-04-24 InnovaPad, LP Methods and Systems for the Integrated Collection of Data for Use in Incident Reports and Insurance Claims and to Related Methods of Performing Emergency Responder Cost Recovery
US9489635B1 (en) 2012-11-01 2016-11-08 Google Inc. Methods and systems for vehicle perception feedback to classify data representative of types of objects and to request feedback regarding such classifications
US9007198B2 (en) 2012-11-02 2015-04-14 Toyota Motor Engineering & Manufacturing North America, Inc. Adaptive Actuator interface for active driver warning
US20140129301A1 (en) 2012-11-07 2014-05-08 Ford Global Technologies, Llc Mobile automotive wireless communication system enabled microbusinesses
US8880239B2 (en) 2012-11-07 2014-11-04 Ford Global Technologies, Llc Credential check and authorization solution for personal vehicle rental
DE102012022336A1 (en) 2012-11-14 2014-05-15 Valeo Schalter Und Sensoren Gmbh Method for carrying out an at least semi-autonomous parking operation of a motor vehicle in a garage, parking assistance system and motor vehicle
US20150006023A1 (en) * 2012-11-16 2015-01-01 Scope Technologies Holdings Ltd System and method for determination of vheicle accident information
DE102012111991A1 (en) * 2012-11-20 2014-05-22 Conti Temic Microelectronic Gmbh Method for a driver assistance application
US20140149148A1 (en) * 2012-11-27 2014-05-29 Terrance Luciani System and method for autonomous insurance selection
US8457880B1 (en) * 2012-11-28 2013-06-04 Cambridge Mobile Telematics Telematics using personal mobile devices
US9031729B2 (en) 2012-11-29 2015-05-12 Volkswagen Ag Method and system for controlling a vehicle
US8825258B2 (en) 2012-11-30 2014-09-02 Google Inc. Engaging and disengaging for autonomous driving
US9008961B2 (en) 2012-11-30 2015-04-14 Google Inc. Determining and displaying auto drive lanes in an autonomous vehicle
US8914225B2 (en) 2012-12-04 2014-12-16 International Business Machines Corporation Managing vehicles on a road network
US20140159856A1 (en) 2012-12-12 2014-06-12 Thorsten Meyer Sensor hierarchy
US8930269B2 (en) 2012-12-17 2015-01-06 State Farm Mutual Automobile Insurance Company System and method to adjust insurance rate based on real-time data about potential vehicle operator impairment
US8981942B2 (en) 2012-12-17 2015-03-17 State Farm Mutual Automobile Insurance Company System and method to monitor and reduce vehicle operator impairment
US9081650B1 (en) 2012-12-19 2015-07-14 Allstate Insurance Company Traffic based driving analysis
US10796510B2 (en) 2012-12-20 2020-10-06 Brett I. Walker Apparatus, systems and methods for monitoring vehicular activity
US9761139B2 (en) 2012-12-20 2017-09-12 Wal-Mart Stores, Inc. Location based parking management system
US9443436B2 (en) 2012-12-20 2016-09-13 The Johns Hopkins University System for testing of autonomy in complex environments
KR101449210B1 (en) 2012-12-27 2014-10-08 현대자동차주식회사 Apparatus for converting driving mode of autonomous vehicle and method thereof
US9665997B2 (en) 2013-01-08 2017-05-30 Gordon*Howard Associates, Inc. Method and system for providing feedback based on driving behavior
US8909428B1 (en) 2013-01-09 2014-12-09 Google Inc. Detecting driver grip on steering wheel
KR102002420B1 (en) 2013-01-18 2019-10-01 삼성전자주식회사 Smart home system with portable gateway
US9049584B2 (en) 2013-01-24 2015-06-02 Ford Global Technologies, Llc Method and system for transmitting data using automated voice when data transmission fails during an emergency call
GB201301710D0 (en) * 2013-01-31 2013-03-20 Cambridge Consultants Condition Monitoring Device
US9149236B2 (en) 2013-02-04 2015-10-06 Intel Corporation Assessment and management of emotional state of a vehicle operator
US20140156492A1 (en) 2013-02-15 2014-06-05 Creditex Group, Inc. System and method for conducting an exchange auction
KR101736306B1 (en) 2013-02-27 2017-05-29 한국전자통신연구원 Apparatus and method for copiloting between vehicle and driver
US20140240132A1 (en) 2013-02-28 2014-08-28 Exmovere Wireless LLC Method and apparatus for determining vehicle operator performance
US10386492B2 (en) 2013-03-07 2019-08-20 Trimble Inc. Verifiable authentication services based on global navigation satellite system (GNSS) signals and personal or computer data
US9454786B1 (en) 2013-03-08 2016-09-27 Allstate Insurance Company Encouraging safe driving using a remote vehicle starter and personalized insurance rates
US8799034B1 (en) * 2013-03-08 2014-08-05 Allstate University Company Automated accident detection, fault attribution, and claims processing
US9208525B2 (en) 2013-03-10 2015-12-08 State Farm Mutual Automobile Insurance Company System and method for determining and monitoring auto insurance incentives
US20140272811A1 (en) 2013-03-13 2014-09-18 Mighty Carma, Inc. System and method for providing driving and vehicle related assistance to a driver
US9122933B2 (en) 2013-03-13 2015-09-01 Mighty Carma, Inc. After market driving assistance system
US20140278837A1 (en) 2013-03-14 2014-09-18 Frederick T. Blumer Method and system for adjusting a charge related to use of a vehicle based on operational data
US20140278574A1 (en) 2013-03-14 2014-09-18 Ernest W. BARBER System and method for developing a driver safety rating
US9830662B1 (en) 2013-03-15 2017-11-28 State Farm Mutual Automobile Insurance Company Split sensing method
EP2972096B1 (en) 2013-03-15 2019-01-09 Volkswagen Aktiengesellschaft Automatic driving route planning application
US9333983B2 (en) * 2013-03-15 2016-05-10 Volkswagen Ag Dual-state steering wheel/input device
US9224293B2 (en) 2013-03-16 2015-12-29 Donald Warren Taylor Apparatus and system for monitoring and managing traffic flow
US8731977B1 (en) 2013-03-15 2014-05-20 Red Mountain Technologies, LLC System and method for analyzing and using vehicle historical data
US20140278840A1 (en) 2013-03-15 2014-09-18 Inrix Inc. Telemetry-based vehicle policy enforcement
US9959687B2 (en) 2013-03-15 2018-05-01 John Lindsay Driver behavior monitoring
US20140278572A1 (en) 2013-03-15 2014-09-18 State Farm Mutual Automobile Insurance Company System and method for routing a vehicle damaged in a crash
US20140279707A1 (en) 2013-03-15 2014-09-18 CAA South Central Ontario System and method for vehicle data analysis
US9310808B2 (en) * 2013-03-15 2016-04-12 Mts Systems Corporation Apparatus and method for autonomous control and balance of a vehicle and for imparting roll and yaw moments on a vehicle for test purposes
US8876535B2 (en) 2013-03-15 2014-11-04 State Farm Mutual Automobile Insurance Company Real-time driver observation and scoring for driver's education
SE540269C2 (en) 2013-03-19 2018-05-22 Scania Cv Ab Device and method for regulating an autonomous vehicle
GB201305067D0 (en) * 2013-03-19 2013-05-01 Massive Analytic Ltd Apparatus for controlling a land vehicle which is self-driving or partially self-driving
US9342074B2 (en) 2013-04-05 2016-05-17 Google Inc. Systems and methods for transitioning control of an autonomous vehicle to a driver
US9141107B2 (en) 2013-04-10 2015-09-22 Google Inc. Mapping active and inactive construction zones for autonomous driving
US9407874B2 (en) 2013-04-30 2016-08-02 Esurance Insurance Services, Inc. Remote claims adjuster
US20150024705A1 (en) 2013-05-01 2015-01-22 Habib Rashidi Recording and reporting device, method, and application
US10247854B2 (en) 2013-05-07 2019-04-02 Waymo Llc Methods and systems for detecting weather conditions using vehicle onboard sensors
US9003196B2 (en) 2013-05-13 2015-04-07 Hoyos Labs Corp. System and method for authorizing access to access-controlled environments
EP3003999B1 (en) 2013-05-29 2017-03-01 NV Bekaert SA Heat resistant separation fabric
US9147353B1 (en) * 2013-05-29 2015-09-29 Allstate Insurance Company Driving analysis using vehicle-to-vehicle communication
US20140358592A1 (en) 2013-05-31 2014-12-04 OneEvent Technologies, LLC Sensors for usage-based property insurance
US8954205B2 (en) * 2013-06-01 2015-02-10 Savari, Inc. System and method for road side equipment of interest selection for active safety applications
US9958854B2 (en) * 2013-06-10 2018-05-01 The Boeing Company Systems and methods for robotic measurement of parts
CN105307904B (en) 2013-06-12 2018-08-03 博世株式会社 Control is used for protecting the control device and control system of the occupant of vehicle or the protective device of pedestrian
KR101515496B1 (en) 2013-06-12 2015-05-04 국민대학교산학협력단 Simulation system for autonomous vehicle for applying obstacle information in virtual reality
CN105308524A (en) 2013-06-28 2016-02-03 通用电气航空系统有限公司 Method for diagnosing a horizontal stabilizer fault
DE102014109079A1 (en) 2013-06-28 2014-12-31 Harman International Industries, Inc. DEVICE AND METHOD FOR DETECTING THE INTEREST OF A DRIVER ON A ADVERTISING ADVERTISEMENT BY PURSUING THE OPERATOR'S VIEWS
US10152999B2 (en) * 2013-07-03 2018-12-11 Avago Technologies International Sales Pte. Limited Systems and methods for correlation based data alignment
US8874301B1 (en) 2013-07-09 2014-10-28 Ford Global Technologies, Llc Autonomous vehicle with driver presence and physiological monitoring
KR101470190B1 (en) 2013-07-09 2014-12-05 현대자동차주식회사 Apparatus for processing trouble of autonomous driving system and method thereof
US20160036899A1 (en) 2013-07-15 2016-02-04 Strawberry Media, Inc. Systems, methods, and apparatuses for implementing an incident response information management solution for first responders
US9053516B2 (en) 2013-07-15 2015-06-09 Jeffrey Stempora Risk assessment using portable devices
US20150161738A1 (en) 2013-12-10 2015-06-11 Advanced Insurance Products & Services, Inc. Method of determining a risk score or insurance cost using risk-related decision-making processes and decision outcomes
US20150025917A1 (en) 2013-07-15 2015-01-22 Advanced Insurance Products & Services, Inc. System and method for determining an underwriting risk, risk score, or price of insurance using cognitive information
US9454150B2 (en) * 2013-07-17 2016-09-27 Toyota Motor Engineering & Manufacturing North America, Inc. Interactive automated driving system
DE102013214383A1 (en) 2013-07-23 2015-01-29 Robert Bosch Gmbh Method and device for providing a collision signal with regard to a vehicle collision, method and device for managing collision data regarding vehicle collisions, and method and device for controlling at least one collision protection device of a vehicle
US20150032581A1 (en) 2013-07-26 2015-01-29 Bank Of America Corporation Use of e-receipts to determine total cost of ownership
JP6429368B2 (en) 2013-08-02 2018-11-28 本田技研工業株式会社 Inter-vehicle communication system and method
US20150039350A1 (en) 2013-08-05 2015-02-05 Ford Global Technologies, Llc Vehicle operations monitoring
US20150045983A1 (en) 2013-08-07 2015-02-12 DriveFactor Methods, Systems and Devices for Obtaining and Utilizing Vehicle Telematics Data
US9135756B2 (en) * 2013-08-14 2015-09-15 Hti Ip, L.L.C. Providing communications between a vehicle control device and a user device via a head unit
US9710858B1 (en) 2013-08-16 2017-07-18 United Services Automobile Association (Usaa) Insurance policy alterations using informatic sensor data
US20150066284A1 (en) 2013-09-05 2015-03-05 Ford Global Technologies, Llc Autonomous vehicle control for impaired driver
AT514754B1 (en) 2013-09-05 2018-06-15 Avl List Gmbh Method and device for optimizing driver assistance systems
US8935036B1 (en) 2013-09-06 2015-01-13 State Farm Mutual Automobile Insurance Company Systems and methods for updating a driving tip model using telematics data
US9898086B2 (en) 2013-09-06 2018-02-20 Immersion Corporation Systems and methods for visual processing of spectrograms to generate haptic effects
EP2848488B2 (en) 2013-09-12 2022-04-13 Volvo Car Corporation Method and arrangement for handover warning in a vehicle having autonomous driving capabilities
EP2849017B1 (en) 2013-09-12 2016-04-20 Volvo Car Corporation Method and arrangement for pick-up point retrieval timing
US10169821B2 (en) 2013-09-20 2019-01-01 Elwha Llc Systems and methods for insurance based upon status of vehicle software
US9424607B2 (en) * 2013-09-20 2016-08-23 Elwha Llc Systems and methods for insurance based upon status of vehicle software
US20150088373A1 (en) 2013-09-23 2015-03-26 The Boeing Company Optical communications and obstacle sensing for autonomous vehicles
US9150224B2 (en) 2013-09-24 2015-10-06 Ford Global Technologies, Llc Transitioning from autonomous vehicle control to to driver control to responding to driver control
DE102013110852A1 (en) * 2013-10-01 2015-04-16 Volkswagen Aktiengesellschaft Method for a driver assistance system of a vehicle
US20150100189A1 (en) 2013-10-07 2015-04-09 Ford Global Technologies, Llc Vehicle-to-infrastructure communication
EP3056394B1 (en) 2013-10-08 2022-11-30 ICTK Holdings Co., Ltd. Vehicle security network device and design method therefor
US20150100191A1 (en) 2013-10-09 2015-04-09 Ford Global Technologies, Llc Monitoring autonomous vehicle steering
US9096199B2 (en) 2013-10-09 2015-08-04 Ford Global Technologies, Llc Monitoring autonomous vehicle braking
FR3012098B1 (en) 2013-10-17 2017-01-13 Renault Sa SYSTEM AND METHOD FOR CONTROLLING VEHICLE WITH DEFECT MANAGEMENT
US9361650B2 (en) 2013-10-18 2016-06-07 State Farm Mutual Automobile Insurance Company Synchronization of vehicle sensor information
US20150112731A1 (en) 2013-10-18 2015-04-23 State Farm Mutual Automobile Insurance Company Risk assessment for an automated vehicle
US20150112800A1 (en) 2013-10-18 2015-04-23 State Farm Mutual Automobile Insurance Company Targeted advertising using vehicle information
US9262787B2 (en) 2013-10-18 2016-02-16 State Farm Mutual Automobile Insurance Company Assessing risk using vehicle environment information
US8954226B1 (en) 2013-10-18 2015-02-10 State Farm Mutual Automobile Insurance Company Systems and methods for visualizing an accident involving a vehicle
US9892567B2 (en) 2013-10-18 2018-02-13 State Farm Mutual Automobile Insurance Company Vehicle sensor collection of other vehicle information
JP5975964B2 (en) 2013-10-18 2016-08-23 富士通株式会社 Information processing program, information processing method, information processing apparatus, and information processing system
US10395318B2 (en) * 2013-10-24 2019-08-27 Hartford Fire Insurance Company System and method for administering insurance discounts for mobile device disabling technology
US9177475B2 (en) 2013-11-04 2015-11-03 Volkswagen Ag Driver behavior based parking availability prediction system and method
US20150127570A1 (en) 2013-11-05 2015-05-07 Hti Ip, Llc Automatic accident reporting device
US9529584B2 (en) 2013-11-06 2016-12-27 General Motors Llc System and method for preparing vehicle for remote reflash event
US20150268665A1 (en) 2013-11-07 2015-09-24 Google Inc. Vehicle communication using audible signals
JP6272347B2 (en) 2013-11-08 2018-01-31 株式会社日立製作所 Autonomous traveling vehicle and autonomous traveling system
DE102013223240B3 (en) 2013-11-14 2014-10-30 Volkswagen Aktiengesellschaft Motor vehicle with occlusion detection for ultrasonic sensors
US9401056B2 (en) 2013-11-19 2016-07-26 At&T Intellectual Property I, L.P. Vehicular simulation
US9517771B2 (en) 2013-11-22 2016-12-13 Ford Global Technologies, Llc Autonomous vehicle modes
US10088844B2 (en) 2013-11-22 2018-10-02 Ford Global Technologies, Llc Wearable computer in an autonomous vehicle
US8977499B1 (en) * 2013-11-22 2015-03-10 Toyota Motor Engineering & Manufacturing North America, Inc. Auditory interface for automated driving system
US9475496B2 (en) 2013-11-22 2016-10-25 Ford Global Technologies, Llc Modified autonomous vehicle settings
US20150149265A1 (en) 2013-11-27 2015-05-28 GM Global Technology Operations LLC Controlled parking of autonomous vehicles
JP6042794B2 (en) 2013-12-03 2016-12-14 本田技研工業株式会社 Vehicle control method
US9123250B2 (en) 2013-12-05 2015-09-01 Elwha Llc Systems and methods for reporting real-time handling characteristics
US20150158495A1 (en) * 2013-12-05 2015-06-11 Elwha Llc Systems and methods for reporting characteristics of operator performance
US20150161894A1 (en) * 2013-12-05 2015-06-11 Elwha Llc Systems and methods for reporting characteristics of automatic-driving software
US9707942B2 (en) * 2013-12-06 2017-07-18 Elwha Llc Systems and methods for determining a robotic status of a driving vehicle
US9164507B2 (en) * 2013-12-06 2015-10-20 Elwha Llc Systems and methods for modeling driving behavior of vehicles
US9747353B2 (en) 2013-12-10 2017-08-29 Sap Se Database content publisher
KR20150070801A (en) 2013-12-17 2015-06-25 현대자동차주식회사 Method for transmitting traffic information using vehicle to vehicle communications
US20150170287A1 (en) * 2013-12-18 2015-06-18 The Travelers Indemnity Company Insurance applications for autonomous vehicles
US20150166069A1 (en) 2013-12-18 2015-06-18 Ford Global Technologies, Llc Autonomous driving style learning
US9715378B2 (en) 2013-12-18 2017-07-25 International Business Machines Corporation Automated software update scheduling
US9406177B2 (en) * 2013-12-20 2016-08-02 Ford Global Technologies, Llc Fault handling in an autonomous vehicle
US9650051B2 (en) 2013-12-22 2017-05-16 Lytx, Inc. Autonomous driving comparison and evaluation
WO2015099679A1 (en) 2013-12-23 2015-07-02 Intel Corporation In-vehicle authorization for autonomous vehicles
KR101475040B1 (en) 2013-12-23 2014-12-24 한국교통대학교산학협력단 Method and System for Providing Social Network Service Based on Traffic Information
US9677529B2 (en) 2013-12-25 2017-06-13 Denso Corporation Vehicle diagnosis system and method
KR20150076627A (en) 2013-12-27 2015-07-07 한국전자통신연구원 System and method for learning driving information in vehicle
US20150187194A1 (en) 2013-12-29 2015-07-02 Keanu Hypolite Device, system, and method of smoke and hazard detection
US20150187019A1 (en) 2013-12-31 2015-07-02 Hartford Fire Insurance Company Systems and method for autonomous vehicle data processing
US20150187016A1 (en) 2013-12-31 2015-07-02 Hartford Fire Insurance Company System and method for telematics based underwriting
US20150187015A1 (en) 2013-12-31 2015-07-02 Hartford Fire Insurance Company System and method for destination based underwriting
US10134091B2 (en) * 2013-12-31 2018-11-20 Hartford Fire Insurance Company System and method for determining driver signatures
US9524156B2 (en) 2014-01-09 2016-12-20 Ford Global Technologies, Llc Flexible feature deployment strategy
US9766874B2 (en) 2014-01-09 2017-09-19 Ford Global Technologies, Llc Autonomous global software update
US20150203107A1 (en) 2014-01-17 2015-07-23 Ford Global Technologies, Llc Autonomous vehicle precipitation detection
US9199642B2 (en) 2014-01-21 2015-12-01 Elwha Llc Vehicle collision management responsive to traction conditions in an avoidance path
US9390451B1 (en) * 2014-01-24 2016-07-12 Allstate Insurance Company Insurance system related to a vehicle-to-vehicle communication system
US10096067B1 (en) * 2014-01-24 2018-10-09 Allstate Insurance Company Reward system related to a vehicle-to-vehicle communication system
US9355423B1 (en) 2014-01-24 2016-05-31 Allstate Insurance Company Reward system related to a vehicle-to-vehicle communication system
WO2015116022A1 (en) 2014-01-28 2015-08-06 GM Global Technology Operations LLC Situational awareness for a vehicle
CA2938378A1 (en) 2014-01-30 2015-08-06 Universidade Do Porto Device and method for self-automated parking lot for autonomous vehicles based on vehicular networking
DE102014001554B4 (en) 2014-02-05 2016-07-14 Audi Ag Method for automatically parking a vehicle and associated control device
US9390567B2 (en) 2014-02-05 2016-07-12 Harman International Industries, Incorporated Self-monitoring and alert system for intelligent vehicle
US9205805B2 (en) 2014-02-14 2015-12-08 International Business Machines Corporation Limitations on the use of an autonomous vehicle
US9079587B1 (en) 2014-02-14 2015-07-14 Ford Global Technologies, Llc Autonomous control in a dense vehicle environment
US9666069B2 (en) * 2014-02-14 2017-05-30 Ford Global Technologies, Llc Autonomous vehicle handling and performance adjustment
US9940676B1 (en) * 2014-02-19 2018-04-10 Allstate Insurance Company Insurance system for analysis of autonomous driving
US10796369B1 (en) * 2014-02-19 2020-10-06 Allstate Insurance Company Determining a property of an insurance policy based on the level of autonomy of a vehicle
US10783586B1 (en) * 2014-02-19 2020-09-22 Allstate Insurance Company Determining a property of an insurance policy based on the density of vehicles
US9709984B2 (en) 2014-02-19 2017-07-18 Lenovo Enterprise Solutions (Singapore) Pte. Ltd. Administering a recall by an autonomous vehicle
US20150235323A1 (en) 2014-02-19 2015-08-20 Himex Limited Automated vehicle crash detection
US10783587B1 (en) * 2014-02-19 2020-09-22 Allstate Insurance Company Determining a driver score based on the driver's response to autonomous features of a vehicle
US10803525B1 (en) * 2014-02-19 2020-10-13 Allstate Insurance Company Determining a property of an insurance policy based on the autonomous features of a vehicle
US9531968B2 (en) * 2014-02-25 2016-12-27 Semiconductor Components Industries, Llc Imagers having image processing circuitry with error detection capabilities
US9915925B2 (en) 2014-02-25 2018-03-13 Honeywell International Inc. Initiated test health management system and method
US10380693B2 (en) * 2014-02-25 2019-08-13 State Farm Mutual Automobile Insurance Company Systems and methods for generating data that is representative of an insurance policy for an autonomous vehicle
US9567007B2 (en) 2014-02-27 2017-02-14 International Business Machines Corporation Identifying cost-effective parking for an autonomous vehicle
US9511764B2 (en) 2014-02-28 2016-12-06 Ford Global Technologies, Llc Semi-autonomous mode control
EP3114574A4 (en) 2014-03-03 2018-03-07 Inrix, Inc. Traffic obstruction detection
EP2915718B1 (en) 2014-03-04 2018-07-11 Volvo Car Corporation Apparatus and method for continuously establishing a boundary for autonomous driving availability and an automotive vehicle comprising such an apparatus
US9734685B2 (en) 2014-03-07 2017-08-15 State Farm Mutual Automobile Insurance Company Vehicle operator emotion management system and method
US9053588B1 (en) 2014-03-13 2015-06-09 Allstate Insurance Company Roadside assistance management
EP2921363A1 (en) 2014-03-18 2015-09-23 Volvo Car Corporation Vehicle, vehicle system and method for increasing safety and/or comfort during autonomous driving
EP2922033B1 (en) 2014-03-18 2018-11-21 Volvo Car Corporation A vehicle sensor diagnosis system and method and a vehicle comprising such a system
US20160189303A1 (en) 2014-03-21 2016-06-30 Gil Emanuel Fuchs Risk Based Automotive Insurance Rating System
EP3127403B8 (en) 2014-04-04 2019-04-10 Signify Holding B.V. System and methods to support autonomous vehicles via environmental perception and sensor calibration and verification
CA2943454C (en) 2014-04-04 2022-08-23 Superpedestrian, Inc. Systems, methods and devices for the operation of electrically motorized vehicles
US9507345B2 (en) 2014-04-10 2016-11-29 Nissan North America, Inc. Vehicle control system and method
US10179588B2 (en) 2014-04-11 2019-01-15 Nissan North America, Inc. Autonomous vehicle control system
US10049408B2 (en) 2014-04-15 2018-08-14 Speedgauge, Inc. Assessing asynchronous authenticated data sources for use in driver risk management
US9135803B1 (en) 2014-04-17 2015-09-15 State Farm Mutual Automobile Insurance Company Advanced vehicle operator intelligence system
US20150310758A1 (en) 2014-04-26 2015-10-29 The Travelers Indemnity Company Systems, methods, and apparatus for generating customized virtual reality experiences
EP2940672B1 (en) 2014-04-29 2018-03-07 Fujitsu Limited Vehicular safety system
US9283847B2 (en) 2014-05-05 2016-03-15 State Farm Mutual Automobile Insurance Company System and method to monitor and alert vehicle operator of impairment
CN106664431A (en) 2014-05-06 2017-05-10 大陆-特韦斯股份有限公司 Method and system for detecting and/or backing up video data in a motor vehicle
US9399445B2 (en) 2014-05-08 2016-07-26 International Business Machines Corporation Delegating control of a vehicle
US10185999B1 (en) 2014-05-20 2019-01-22 State Farm Mutual Automobile Insurance Company Autonomous feature use monitoring and telematics
US9972054B1 (en) 2014-05-20 2018-05-15 State Farm Mutual Automobile Insurance Company Accident fault determination for autonomous vehicles
US10185997B1 (en) 2014-05-20 2019-01-22 State Farm Mutual Automobile Insurance Company Accident fault determination for autonomous vehicles
US9475422B2 (en) 2014-05-22 2016-10-25 Applied Invention, Llc Communication between autonomous vehicle and external observers
US9631933B1 (en) 2014-05-23 2017-04-25 Google Inc. Specifying unavailable locations for autonomous vehicles
DE102014210147A1 (en) 2014-05-27 2015-12-03 Continental Teves Ag & Co. Ohg Vehicle control system for autonomous guidance of a vehicle
KR102186350B1 (en) 2014-05-30 2020-12-03 현대모비스 주식회사 Apparatus and method for requesting emergency call about vehicle accident using driving information of vehicle
US10166916B2 (en) 2014-05-30 2019-01-01 State Farm Mutual Automobile Insurance Company Systems and methods for determining a vehicle is at an elevated risk for an animal collision
US9656606B1 (en) 2014-05-30 2017-05-23 State Farm Mutual Automobile Insurance Company Systems and methods for alerting a driver to vehicle collision risks
US20150356797A1 (en) 2014-06-05 2015-12-10 International Business Machines Corporation Virtual key fob with transferable user data profile
US9282447B2 (en) 2014-06-12 2016-03-08 General Motors Llc Vehicle incident response method and system
DE102014211557A1 (en) 2014-06-17 2015-12-31 Robert Bosch Gmbh Valet parking procedure and system
US9465346B2 (en) 2014-06-24 2016-10-11 Kabushiki Kaisha Toshiba Metallic color image forming apparatus and metallic color image forming method
EP3161730A4 (en) 2014-06-30 2018-02-28 Evolving Machine Intelligence Pty Ltd. A system and method for modelling system behaviour
US9361735B1 (en) 2014-07-11 2016-06-07 State Farm Mutual Automobile Insurance Company Method and system of using spatial sensors on vehicle frame to determine crash information
US9805602B2 (en) 2014-07-21 2017-10-31 Ford Global Technologies, Llc Parking service
US9972184B2 (en) 2014-07-24 2018-05-15 State Farm Mutual Automobile Insurance Company Systems and methods for monitoring a vehicle operator and for monitoring an operating environment within the vehicle
US9766625B2 (en) 2014-07-25 2017-09-19 Here Global B.V. Personalized driving of autonomously driven vehicles
US9189897B1 (en) 2014-07-28 2015-11-17 Here Global B.V. Personalized driving ranking and alerting
US9282430B1 (en) 2014-07-30 2016-03-08 Allstate Insurance Company Roadside assistance service provider assignment system
US10354329B2 (en) 2014-08-06 2019-07-16 Hartford Fire Insurance Company Smart sensors for roof ice formation and property condition monitoring
US9514651B2 (en) 2014-08-19 2016-12-06 Here Global B.V. Optimal warning distance
US9948898B2 (en) 2014-08-22 2018-04-17 Verizon Patent And Licensing Inc. Using aerial imaging to provide supplemental information about a location
US9997077B2 (en) 2014-09-04 2018-06-12 Honda Motor Co., Ltd. Vehicle operation assistance
US10377303B2 (en) 2014-09-04 2019-08-13 Toyota Motor Engineering & Manufacturing North America, Inc. Management of driver and vehicle modes for semi-autonomous driving systems
US9773281B1 (en) * 2014-09-16 2017-09-26 Allstate Insurance Company Accident detection and recovery
US20160086265A1 (en) 2014-09-20 2016-03-24 Edgar Parker, JR. System and Method of Relative Channel Capacity based securities trading
US10102590B1 (en) 2014-10-02 2018-10-16 United Services Automobile Association (Usaa) Systems and methods for unmanned vehicle management
US9663112B2 (en) 2014-10-09 2017-05-30 Ford Global Technologies, Llc Adaptive driver identification fusion
US9716758B2 (en) 2014-10-13 2017-07-25 General Motors Llc Network-coordinated DRx transmission reduction for a network access device of a telematics-equipped vehicle
US9377315B2 (en) 2014-10-22 2016-06-28 Myine Electronics, Inc. System and method to provide valet instructions for a self-driving vehicle
US20160116913A1 (en) 2014-10-23 2016-04-28 James E. Niles Autonomous vehicle environment detection system
US9424751B2 (en) 2014-10-24 2016-08-23 Telogis, Inc. Systems and methods for performing driver and vehicle analysis and alerting
SG10201407100PA (en) 2014-10-30 2016-05-30 Nec Asia Pacific Pte Ltd System For Monitoring Event Related Data
US9547985B2 (en) 2014-11-05 2017-01-17 Here Global B.V. Method and apparatus for providing access to autonomous vehicles based on user context
US9804594B2 (en) 2014-11-07 2017-10-31 Clearpath Robotics, Inc. Self-calibrating sensors and actuators for unmanned vehicles
US9430944B2 (en) 2014-11-12 2016-08-30 GM Global Technology Operations LLC Method and apparatus for determining traffic safety events using vehicular participative sensing systems
US10915965B1 (en) 2014-11-13 2021-02-09 State Farm Mutual Automobile Insurance Company Autonomous vehicle insurance based upon usage
US9524648B1 (en) 2014-11-17 2016-12-20 Amazon Technologies, Inc. Countermeasures for threats to an uncrewed autonomous vehicle
US9466154B2 (en) 2014-11-21 2016-10-11 International Business Machines Corporation Automated service management
US10614726B2 (en) 2014-12-08 2020-04-07 Life Long Driver, Llc Behaviorally-based crash avoidance system
US20160187368A1 (en) 2014-12-30 2016-06-30 Google Inc. Systems and methods of detecting failure of an opening sensor
US10429177B2 (en) 2014-12-30 2019-10-01 Google Llc Blocked sensor detection and notification
US9712549B2 (en) 2015-01-08 2017-07-18 Imam Abdulrahman Bin Faisal University System, apparatus, and method for detecting home anomalies
US10198772B2 (en) * 2015-01-14 2019-02-05 Tata Consultancy Services Limited Driver assessment and recommendation system in a vehicle
US9841286B1 (en) 2015-01-20 2017-12-12 State Farm Mutual Automobile Insurance Company Using train telematics data to reduce accident risk
US9361599B1 (en) 2015-01-28 2016-06-07 Allstate Insurance Company Risk unit based policies
US9390452B1 (en) 2015-01-28 2016-07-12 Allstate Insurance Company Risk unit based policies
US10216196B2 (en) 2015-02-01 2019-02-26 Prosper Technology, Llc Methods to operate autonomous vehicles to pilot vehicles in groups or convoys
US20160231746A1 (en) 2015-02-06 2016-08-11 Delphi Technologies, Inc. System And Method To Operate An Automated Vehicle
US10678261B2 (en) 2015-02-06 2020-06-09 Aptiv Technologies Limited Method and apparatus for controlling an autonomous vehicle
US9942056B2 (en) 2015-02-19 2018-04-10 Vivint, Inc. Methods and systems for automatically monitoring user activity
US10049505B1 (en) 2015-02-27 2018-08-14 State Farm Mutual Automobile Insurance Company Systems and methods for maintaining a self-driving vehicle
US9701305B2 (en) 2015-03-10 2017-07-11 GM Global Technology Operations LLC Automatic valet parking
DE102015204359A1 (en) 2015-03-11 2016-09-15 Robert Bosch Gmbh Driving a motor vehicle in a parking lot
US20170011467A1 (en) * 2015-03-14 2017-01-12 Telanon, Inc. Methods and Apparatus for Remote Collection of Sensor Data for Vehicle Trips with High-Integrity Vehicle Identification
KR101675306B1 (en) 2015-03-20 2016-11-11 현대자동차주식회사 Accident information manage apparatus, vehicle having the same and method for managing accident information
KR101656808B1 (en) 2015-03-20 2016-09-22 현대자동차주식회사 Accident information manage apparatus, vehicle having the same and method for managing accident information
US9371072B1 (en) 2015-03-24 2016-06-21 Toyota Jidosha Kabushiki Kaisha Lane quality service
EP3278317B1 (en) 2015-03-31 2022-11-16 Sony Group Corporation Method and electronic device
US20160292679A1 (en) 2015-04-03 2016-10-06 Uber Technologies, Inc. Transport monitoring
US9809163B2 (en) 2015-04-14 2017-11-07 Harman International Industries, Incorporation Techniques for transmitting an alert towards a target area
US9643606B2 (en) 2015-04-14 2017-05-09 Ford Global Technologies, Llc Vehicle control in traffic conditions
US9522598B2 (en) 2015-04-16 2016-12-20 Verizon Patent And Licensing Inc. Vehicle occupant emergency system
US9694765B2 (en) 2015-04-20 2017-07-04 Hitachi, Ltd. Control system for an automotive vehicle
US9874451B2 (en) 2015-04-21 2018-01-23 Here Global B.V. Fresh hybrid routing independent of map version and provider
US20160314224A1 (en) 2015-04-24 2016-10-27 Northrop Grumman Systems Corporation Autonomous vehicle simulation system
US9505494B1 (en) 2015-04-30 2016-11-29 Allstate Insurance Company Enhanced unmanned aerial vehicles for damage inspection
DE102015208914B4 (en) 2015-04-30 2020-09-24 Volkswagen Aktiengesellschaft Procedure for assisting a vehicle
US10102586B1 (en) 2015-04-30 2018-10-16 Allstate Insurance Company Enhanced unmanned aerial vehicles for damage inspection
US9948477B2 (en) 2015-05-12 2018-04-17 Echostar Technologies International Corporation Home automation weather detection
CN104933293A (en) 2015-05-22 2015-09-23 小米科技有限责任公司 Road information processing method and device
US9733096B2 (en) 2015-06-22 2017-08-15 Waymo Llc Determining pickup and destination locations for autonomous vehicles
US9511767B1 (en) 2015-07-01 2016-12-06 Toyota Motor Engineering & Manufacturing North America, Inc. Autonomous vehicle action planning using behavior prediction
US20170015263A1 (en) 2015-07-14 2017-01-19 Ford Global Technologies, Llc Vehicle Emergency Broadcast
US20170017734A1 (en) 2015-07-15 2017-01-19 Ford Global Technologies, Llc Crowdsourced Event Reporting and Reconstruction
US9785145B2 (en) 2015-08-07 2017-10-10 International Business Machines Corporation Controlling driving modes of self-driving vehicles
US9805605B2 (en) 2015-08-12 2017-10-31 Madhusoodhan Ramanujam Using autonomous vehicles in a taxi service
US10023231B2 (en) 2015-08-12 2018-07-17 Madhusoodhan Ramanujam Parking autonomous vehicles
US10220705B2 (en) 2015-08-12 2019-03-05 Madhusoodhan Ramanujam Sharing autonomous vehicles
US9805519B2 (en) 2015-08-12 2017-10-31 Madhusoodhan Ramanujam Performing services on autonomous vehicles
US20210272207A1 (en) 2015-08-28 2021-09-02 State Farm Mutual Automobile Insurance Company Vehicular driver profiles and discounts
DE102015216494A1 (en) 2015-08-28 2017-03-02 Robert Bosch Gmbh Method and device for detecting at least one sensor function of at least one first sensor of at least one first vehicle
US10013697B1 (en) 2015-09-02 2018-07-03 State Farm Mutual Automobile Insurance Company Systems and methods for managing and processing vehicle operator accounts based on vehicle operation data
WO2017040924A1 (en) 2015-09-04 2017-03-09 Robert Bosch Gmbh Billboard display and method for selectively displaying advertisements by sensing demographic information of occupants of vehicles
US9587952B1 (en) 2015-09-09 2017-03-07 Allstate Insurance Company Altering autonomous or semi-autonomous vehicle operation based on route traversal values
US10181266B2 (en) 2015-09-11 2019-01-15 Sony Corporation System and method to provide driving assistance
US10150448B2 (en) 2015-09-18 2018-12-11 Ford Global Technologies. Llc Autonomous vehicle unauthorized passenger or object detection
KR20170034023A (en) 2015-09-18 2017-03-28 삼성전자주식회사 Method and apparatus for resoruce allocaton in v2x communicaton system
US9847033B1 (en) 2015-09-25 2017-12-19 Amazon Technologies, Inc. Communication of navigation data spoofing between unmanned vehicles
US9834224B2 (en) 2015-10-15 2017-12-05 International Business Machines Corporation Controlling driving modes of self-driving vehicles
DE102015220823B4 (en) 2015-10-26 2024-01-25 Robert Bosch Gmbh Method for detecting a malfunction of at least one sensor for controlling a restraint device of a vehicle, control device and vehicle
US9632502B1 (en) 2015-11-04 2017-04-25 Zoox, Inc. Machine-learning systems and techniques to optimize teleoperation and/or planner decisions
US9720415B2 (en) 2015-11-04 2017-08-01 Zoox, Inc. Sensor-based object-detection optimization for autonomous vehicles
US10737577B2 (en) 2015-11-04 2020-08-11 Ford Global Technologies, Llc Control strategy for charging electrified vehicle over multiple locations of a drive route
US9754490B2 (en) 2015-11-04 2017-09-05 Zoox, Inc. Software application to request and control an autonomous vehicle service
US9958864B2 (en) 2015-11-04 2018-05-01 Zoox, Inc. Coordination of dispatching and maintaining fleet of autonomous vehicles
US9944192B2 (en) 2015-11-13 2018-04-17 Nio Usa, Inc. Electric vehicle charging station system and method of use
US10242558B2 (en) 2015-11-16 2019-03-26 Google Llc Systems and methods for handling latent anomalies
US9939279B2 (en) 2015-11-16 2018-04-10 Uber Technologies, Inc. Method and system for shared transport
GB2546143B (en) 2015-11-23 2020-04-29 Walmart Apollo Llc Navigating a customer to a parking space
US20170148101A1 (en) 2015-11-23 2017-05-25 CSI Holdings I LLC Damage assessment and repair based on objective surface data
KR101786228B1 (en) 2015-12-01 2017-10-18 현대자동차주식회사 Failure dignosis method for vehicle
US9709988B2 (en) 2015-12-09 2017-07-18 Ford Global Technologies, Llc Identification of acceptable vehicle charge stations
KR101786237B1 (en) 2015-12-09 2017-10-17 현대자동차주식회사 Apparatus and method for processing failure detection and calibration of sensor in driver assist system
US10459440B2 (en) 2016-01-04 2019-10-29 GM Global Technology Operations LLC System and method for remotely assisting autonomous vehicle operation
US11119477B1 (en) 2016-01-22 2021-09-14 State Farm Mutual Automobile Insurance Company Anomalous condition detection and response for autonomous vehicles
US9940834B1 (en) 2016-01-22 2018-04-10 State Farm Mutual Automobile Insurance Company Autonomous vehicle application
US10134278B1 (en) 2016-01-22 2018-11-20 State Farm Mutual Automobile Insurance Company Autonomous vehicle application
EP3417240A4 (en) 2016-02-15 2019-07-17 Allstate Insurance Company Early notification of non-autonomous area
EP3417242B1 (en) 2016-02-15 2022-12-21 Allstate Insurance Company Real time risk assessment and operational changes with semi-autonomous vehicles
US10026317B2 (en) 2016-02-25 2018-07-17 Ford Global Technologies, Llc Autonomous probability control
US10289113B2 (en) 2016-02-25 2019-05-14 Ford Global Technologies, Llc Autonomous occupant attention-based control
US9986404B2 (en) 2016-02-26 2018-05-29 Rapidsos, Inc. Systems and methods for emergency communications amongst groups of devices based on shared data
US10319157B2 (en) 2016-03-22 2019-06-11 GM Global Technology Operations LLC System and method for automatic maintenance
US9964948B2 (en) 2016-04-20 2018-05-08 The Florida International University Board Of Trustees Remote control and concierge service for an autonomous transit vehicle fleet
US10467824B2 (en) 2016-04-26 2019-11-05 Walter Steven Rosenbaum Method for determining driving characteristics of a vehicle and vehicle analyzing system
US9725036B1 (en) 2016-06-28 2017-08-08 Toyota Motor Engineering & Manufacturing North America, Inc. Wake-up alerts for sleeping vehicle occupants
US20180013831A1 (en) 2016-07-11 2018-01-11 Hcl Technologies Limited Alerting one or more service providers based on analysis of sensor data
US20180053411A1 (en) 2016-08-19 2018-02-22 Delphi Technologies, Inc. Emergency communication system for automated vehicles
US20190005464A1 (en) 2016-08-31 2019-01-03 Faraday&Future Inc. System and method for scheduling vehicle maintenance services
US20180080995A1 (en) 2016-09-20 2018-03-22 Faraday&Future Inc. Notification system and method for providing remaining running time of a battery
US20180091981A1 (en) 2016-09-23 2018-03-29 Board Of Trustees Of The University Of Arkansas Smart vehicular hybrid network systems and applications of same
US11155267B2 (en) 2016-10-11 2021-10-26 Samsung Electronics Co., Ltd. Mobile sensor platform
US9817400B1 (en) 2016-12-14 2017-11-14 Uber Technologies, Inc. Vehicle servicing system
WO2018125928A1 (en) 2016-12-29 2018-07-05 DeepScale, Inc. Multi-channel sensor simulation for autonomous control systems
US10095239B1 (en) 2017-03-31 2018-10-09 Uber Technologies, Inc. Autonomous vehicle paletization system
US10889196B2 (en) 2017-06-02 2021-01-12 CarFlex Corporation Autonomous vehicle servicing and energy management
US10510195B2 (en) 2017-06-29 2019-12-17 Tesla, Inc. System and method for monitoring stress cycles
US10571917B2 (en) 2017-11-10 2020-02-25 Uatc, Llc Systems and methods for providing a vehicle service via a transportation network for autonomous vehicles
US20190146491A1 (en) 2017-11-10 2019-05-16 GM Global Technology Operations LLC In-vehicle system to communicate with passengers

Patent Citations (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH063723A (en) * 1992-06-23 1994-01-14 Olympus Optical Co Ltd Image processor
WO2000013947A2 (en) * 1998-09-09 2000-03-16 Siemens Automotive Corporation Remote control key system having keyless entry functions and a vehicle immobilizing function in common keyhead
US20090109037A1 (en) * 2000-08-11 2009-04-30 Telanon, Inc. Automated consumer to business electronic marketplace system
US20040088089A1 (en) * 2000-09-20 2004-05-06 Wolf-Dieterich Bauer Device and method for detecting driving data of a motor vehicle comprising an electronically controllable drive train
US20030069761A1 (en) * 2001-10-10 2003-04-10 Increment P Corporation, Shuji Kawakami, And Nobuhiro Shoji System for taking out insurance policy, method of taking out insurance policy, server apparatus and terminal apparatus
US20030089183A1 (en) * 2001-11-13 2003-05-15 Jacobsen Robert A. Apparatus and method for non-destructive inspection of large structures
US20050043856A1 (en) * 2002-10-04 2005-02-24 Bautista Dexter V. Ultrasonic sensor for detecting the dispensing of a product
FR2863214A1 (en) * 2003-12-05 2005-06-10 Toyota Motor Co Ltd DEVICE AND METHOD FOR CONTROLLING DECELERATION FOR A VEHICLE
US20070088454A1 (en) * 2004-10-25 2007-04-19 Ford Motor Company System and method for troubleshooting a machine
US10705533B1 (en) * 2005-05-31 2020-07-07 Richard Anthony Bishel Autonomous lawnmower
WO2008154097A1 (en) * 2007-05-07 2008-12-18 The Boeing Company Health management of single or multi-platform systems
US7525417B1 (en) * 2008-05-16 2009-04-28 International Business Machines Corporation Method and system for transitive turn signal and braking indication
US20120046841A1 (en) * 2009-04-21 2012-02-23 Zf Friedrichshafen Ag Method for operating a vehicle having a freewheel mode or a rolling mode
US20120123806A1 (en) * 2009-12-31 2012-05-17 Schumann Jr Douglas D Systems and methods for providing a safety score associated with a user location
US20120109692A1 (en) * 2010-05-17 2012-05-03 The Travelers Indemnity Company Monitoring customer-selected vehicle parameters in accordance with customer preferences
US8712909B1 (en) * 2011-02-23 2014-04-29 United Services Automobile Association Systems and methods for managing fleet services
US8630806B1 (en) * 2011-10-20 2014-01-14 Google Inc. Image processing for vehicle control
US8560165B2 (en) * 2012-01-17 2013-10-15 GM Global Technology Operations LLC Co-operative on-board and off-board component and system diagnosis and prognosis
US20230290251A1 (en) * 2012-09-27 2023-09-14 Waymo Llc Determining changes in a driving environment based on vehicle behavior
US9274525B1 (en) * 2012-09-28 2016-03-01 Google Inc. Detecting sensor degradation by actively controlling an autonomous vehicle
US10713726B1 (en) * 2013-01-13 2020-07-14 United Services Automobile Association (Usaa) Determining insurance policy modifications using informatic sensor data
DE102013200391A1 (en) * 2013-01-14 2014-07-17 Robert Bosch Gmbh Method for adaptive cruise control of motor vehicle, involves automatically regulating actual speed of motor vehicle to predetermined target speed and automatically changing target speed and target distance
US9019092B1 (en) * 2013-03-08 2015-04-28 Allstate Insurance Company Determining whether a vehicle is parked for automated accident detection, fault attribution, and claims processing
US20170190335A1 (en) * 2013-04-26 2017-07-06 Carla R. Gillett Vehicle Comprising Autonomous Steering Column System
US11716488B2 (en) * 2019-09-20 2023-08-01 Qualcomm Incorporated Subpicture signaling in high-level syntax for video coding

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