EP3013643A2 - Onboard vehicle accident detection and damage estimation system and method of use - Google Patents
Onboard vehicle accident detection and damage estimation system and method of useInfo
- Publication number
- EP3013643A2 EP3013643A2 EP14818449.2A EP14818449A EP3013643A2 EP 3013643 A2 EP3013643 A2 EP 3013643A2 EP 14818449 A EP14818449 A EP 14818449A EP 3013643 A2 EP3013643 A2 EP 3013643A2
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- European Patent Office
- Prior art keywords
- vehicle
- accident
- data
- information
- assessment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3461—Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME 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
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME 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
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0841—Registering performance data
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/016—Personal emergency signalling and security systems
Definitions
- This invention relates to an in-vehicle system and method to detect, analyze and transmit information about an accident.
- the system monitors onboard sensor input then compares that input with predetermined stored relationships (patterns) to detect accidents and to access resulting damage and injury.
- damage/injury assessment is then wirelessly transmitted (if feasible) to interested parties such as first responders and insurance carriers.
- the invention also relates to method of initial creation, update and maintenance of the system and supporting database. Background;
- This information can then be relayed digitally (in standard formats) or manually if necessary to interested parties. This information can then be transmitted to, for example, to insurance carriers for the vehicle, emergency responders, service technicians, and towing and parts providers when and if long range wireless communications is available.
- a large amount of data is necessary to accurately and rapidly assess the damage resulting from a vehicle accident. Parts of these data may be specific to both the vehicle, location, and driver of the vehicle. To make pertinent vehicle information available within the vehicle, part of this invention is the methodology to parse and download that pertinent information periodically to the vehicle.
- Real time information may be much more relevant to risk. For example, if the road is icy, the likelihood of making a claim is potentially higher, than if the only information available is that is likely to be icy at the timeframe when driving.
- a dynamic rating system that is continually updated and also has a real-time component, it is further possible to compel drivers to adjust driving habits based on the real-time information to reduce the risk. For example, if a particular route is known to be icy, and the course the driver is taking is being monitored, and the monitoring system further suggest an alternate non- icy route, then the driver can be rewarded for avoiding risky conditions by a reduced premium, or by monthly rebate checks or similar.
- Real-time information can come from a variety of sources such as wireless acquired weather information and traffic reports. This information can further be statistically aggregated to produce historical weather / traffic risk information likelihood indices that are spatially and temporally indexed. Metadata associated with the historical information can then be used to cull older information and continually update the indices with the latest information. Also continuous, real time, accumulation of accident reports with root causes can be helpful to asses and distribute that risk across the total driving space of some geographic region.
- the driving risk could be displayed much like traffic information on a map with high risk being displayed on routed segments in a red color and using other colors for route segments of lesser risk.
- Accident Pattern A known isolated or sequence of sensor readings that are indicative of a vehicle accident or collision occurring. For, example, it may be observed that in a front end collision that the ABS brakes are always applied followed by a rapid deceleration in the direction of travel and finally followed by a complete stoppage of the vehicle.
- Damage Pattern Once it is established that an accident has occurred by observation of an accident pattern occurring, sensor output before, during and after the accident is compared to predefined damage patterns which comprise a sequence and/or collection of sensors readings that exhibit behavior indicative of particular damage being caused.
- the level of damage for example, may be associated with the time interval between the application of the ABS brakes followed by the stoppage of the vehicle.
- Driver Pattern A pattern established that is indicative of driving behavior. For example, frequent fast acceleration from a complete stop may be indicative of reckless driving; comparison of a gps trace while driving and determining that the driver is frequently veering over the median may be indicative of distracted driving. Likewise obeying the speed limit and no undue acceleration would be indicative of a good driver.
- On-board Refers to being part of the vehicle or contained in the vehicle. An onboard sensor therefore could be a sensor that is integral to the engine such as an oxygen sensor, for example, and also sensor in installed aftermarket equipment or sensors in a smartphone that resides in the vehicle. Examples of these type of sensors would be an accelerometer or gps. Processors used to query, record and analyze information can likewise be part of a stock vehicle, and add-on product or a mobile device that resides in the vehicle.
- Transfer Function If an initial database does not contain sensor data from vehicle on-board sensors, but it is built with information transferred from accident reports, then it is necessary to equate how a verbal observation relates to a sensor measurement. For example, perhaps a description of a right front quarter panel being replaced would be assumed to be equivalent to an impact at 45 degrees from the longitudinal centerline of the vehicle with a force of the weight of the vehicle / 5g's.
- Driver Insurance Risk The probability that an insured will make a claim and for how much given a variety of measured factors. It could also refer simply to the probability of being in an accident.
- Transportation Network A system of road, streets, paths, sidewalks, trails, waterways or other ways that a vehicle or pedestrian travels along.
- a transportation network can be subdivided by the type of vehicle or pedestrian that is intended to be used for.
- roads and streets may be used by cars, trucks and busses.
- Trails and sidewalks may be used by pedestrians and perhaps bicycles.
- Transportation networks are generally stored in a Geographic information System that documents the location and interaction of various components of the transportation network. Attribution is also associated with the various components of the network.
- Transportation Element Is a distinct component of a transportation network that has an associated geographic coordinate/s. Examples of elements are road segments where the road begins and ends at an intersection; an intersection between two or more roads; or the boundary of a lake.
- Attribution associated with a transportation network includes any piece of information that can be related to a spatially referenced element or component of the
- Attribution in addition to being spatially referenced may have a temporal (time) component expressed as, for example, time of day, time of week, or time of year. An example of this is the speed limit in a school zone.
- Metadata is a special kind of attribution associated with the quality of components of transportation network. Metadata can be associated with individual geographic components, attribution or the source of the geography or attribution. Metadata may be associated with precision or accuracy of the components or source. Metadata may have a component that list the age of the source.
- a maneuver is an attribute associated with an action that can be either perform or not performed and which is associated with one or more components of a transportation network. For example, a no-left-turn at an intersection is an example of a prohibited maneuver.
- a complex maneuver is generally associated with more than one component of a transportation network - for example, what is known as a Michigan Left Turn, in which a vehicle desires to turn left at an intersection, but in order to do this has to turn right, cross one or more lanes , then cross a median on an avenue, then turn left, is a complex maneuver.
- Index One or more values used to multiply or otherwise adjust up or down a baseline value. For example if a prospective insured base premium is $100, discounts and/or increases to the base may be applied by multiplying the base by a crash index, a driver age index, a safe driving index or a single index that is based on a number of parameters.
- Parameters Any factor that may be directly or indirectly be related to insurance risk, accident pattern, injury pattern, damage pattern or driver pattern.
- Multivariate Analysis A class of statistical analysis used to determine the relevance of one or more parameters in predicting an outcome and used to build a predictive function base on one or more of the analyzed parameters. In this case the outcome is the prediction of insurance risk.
- transportation network over a given time. This may be further subdivided based on weather conditions and/or time of day, time of week or based on other attributes that may influence accident occurrence.
- Incident A single occurrence of a measured parameter.
- an individual accident report is an incident of the parameter accidents;
- a recorded speed of an individual driver along a segment of road is an incident of speed of travel for that segment.
- Granularity This term is used to refer to the specificity of either an attribute or index. For example, if an accident count is based simply on the transportation element it took place on, it is less granular than if the accident count is based on the location (element) and the time.
- Insurance Risk This term is used collectively for all embodiments of the present invention to encompass the desired outcome of an insurance risk model. Examples of desired output are the probability of: having an accident, making an insurance claims, or making an insurance claim within defined monetary limits. [0036] Below are examples of elements of a vehicle insurance risk database. Some or all of these elements may be used to develop a risk model or risk indices.
- Standard GIS road network including:
- Geography typically stored as a series of end nodes locations, and a series of shape points (internal points that define the location of the segment) or as a geometric function.
- Geography may be stored as either a singularity or a series of point and lines which make up a complex intersection (such as a highway cloverleaf)
- Attributes are stored that are associated with the intersection and/or the connecting segments
- Geography usually stored as a reference to one or more geographic components that make up the maneuver
- Attribution Examples (all attributes may have multiple values base on time and may also have metadata associate with them):
- Previous Claims location; type of claim (accident; vandalism; car-jacking); amount of claim; type and age of vehicle.
- Time Series of sensor data before during and after an accident or indicative of a driver pattern
- the present invention pertains to an in-vehicle accident detection, analysis and notification system.
- An additional usage of the system is for monitoring driving behavior; developing insurance premiums based on the monitoring and also effecting change in driver behavior than make a driver safer and less of an insurance risk.
- the system comprises one or more of the following components:
- a vehicle on-board system comprises:
- a processor which monitors and analyses onboard sensors used to detect vehicle activity and driver behavior
- an on-board database comprising:
- Patterns for the individual vehicle type, used to analyze sensor data to detect accidents and to assess resulting injury and damage and useful to predict driver behavior and driver / insurance risk;
- iii one or more of several data transmission components which can include both short range wireless (for example Bluetooth), long range wireless transmission (for example mobile service) and wired data communication component - which can communicate with external servers to transmit both raw sensor data and damage/injury estimation and to provide software and database updates to the vehicle on-board system.
- short range wireless for example Bluetooth
- long range wireless transmission for example mobile service
- wired data communication component - which can communicate with external servers to transmit both raw sensor data and damage/injury estimation and to provide software and database updates to the vehicle on-board system.
- a central server comprising : i. One or more computers;
- Vehicle onboard databases facilitates the analysis of the type and severity and potential cost of repair/injury treatment of the accident and also facilitates the immediate notification of the accident occurrence and assessment to interested parties.
- An object of the present invention is a method to develop a database comprising parameters that may be related to accident detection, accident categorization, insurance risk to be used for vehicle insurance rating and/or pricing and furthermore, where the parameters are related to transportation network elements and/or driving behavior, and/or to vehicle type and characteristics.
- Another object of the invention is to determine which parameters or combination of parameters best able to detect accidents, to categorize accidents, and to determine insurance risk for individual drivers.
- a further object of the present invention is a maintenance and update method for the above mentioned databases.
- Yet another object of the present invention is to track and parameterize the driving habits of individual drivers and to compare those driving habits to historical parameters and habits of other driver in order to predict individual insurance risk.
- Figure 1 illustrates a vehicle on-board system for acquiring, analyzing and transmitting onboard sensor data.
- Figure 2 illustrates the process of pattern detection in sensor data.
- Figure 3 illustrates a method of determining insurance risk.
- Figure 4 illustrates a method of determining models or patterns.
- Figure 5 illustrates a method for generating a hazard index.
- Figure 6 is a flowchart for using a system to adjust insurance rates based on risk
- One or more servers provide the functions in the form of web services or similar of:
- the central service /server or servers are in periodic communication with the vehicle onboard systems.
- the database repository needs to contain all pertinent information and raw data to relate sensor data (or data quantified from text based accident reports) and other pertinent measures for detection of accidents and damage and injury assessments in addition to driver behaviour and estimation of insurance risk. It also can contain information to expedite repair of accident damage and to facilitate treatment of passenger injury.
- the central database may be hosted in a single facility or server or may be hosted in several facilities that are connected via hyper-links and indices.
- data on insurance pay-outs may be in an insurance company database and details of related accidents may be in an accidents report database (at the police station) and the two are linked by the Vehicle Identification Number (VIN) of the automobile and th date of the accident.
- VIN Vehicle Identification Number
- Both the vehicle on-board database and the central database comprise one of more of the data below.
- the on-board database may be vehicle specific and may only contain pertinent information to the vehicle in question.
- the following list comprises information that may be stored
- Figure 1 illustrates an embodiment of the components on-board a vehicle utilized to acquire sensor data, analyze that sensor data indicative of vehicle action and driver behavior.
- the system can be provided all or in part as a portable device (e.g., a smartphone, a tablet computer, computer, or other portable device).
- the overall system comprises vehicle on-board systems for many vehicles and one or more central processor/s and data repository/s.
- a vehicle on-board system Figuer 1 comprises several functionalities that can be implemented as individual components that communicate with one another via wireless or wired connections or a single or few components with modules implemented in software or firmware.
- the components can be permanently installed in a vehicle or be a mobile device that can be used in more than one vehicle or a combination of components that are both permanently installed and removable.
- sensors can be integral to the vehicle or added on or part of another device that resides in the vehicle or any combination.
- a pattern recognition module 106 matching sensor output or derivatives of sensor output to patterns that are indicative of an accident event that has occurred and/or patterns that can be used to predict resulting damage and/or injury associated with an accident events .
- On-board Sensors 102 Data Collection Devices
- a data acquisition module 104 A data acquisition module 104
- Pattern recognition module 106 for accident detection and damage/injury assessment On-board Database 112 containing:
- Communication module 114 used between on-board systems and components and/or external server/s and services
- the on-board sensors 102 comprise one or more of:
- External Services 110 that are monitored comprise one or more of:
- a pattern recognition module 106 is configured with one or more defined operating patterns, each of which operating patterns reflects either a known change in vehicle status, or a known vehicle operating or driving behavior. For example, a vehicle responds in a physically- measurable manner to driver-based driving actions, e.g., by the driver turning the vehicle sharply at a corner.
- An accelerometer can be similarly used to register the sudden deceleration or impact of a collision, and its direction. This enables the system to associate patterns with certain vehicle status or operations, or with historical records for a particular vehicle or for similar vehicles.
- Individual vehicle on-board databases 112 may be a subset of the central database having specific information for a make, model or class of vehicle. In an embodiment, information is also specific to the geographic area the vehicle is in. In an embodiment, to create a vehicle on-board database, a query, based on the Vehicle Identification Number (VIN) of the vehicle, is made to the central database that allows selection of only pertinent data. In an embodiment, the list of equipment that is part of a vehicle can be modified to account for aftermarket replacement parts (for example a new sound system). Depending on communications system that is implemented within the vehicle and the bandwidth available, location specific information can be periodically updated based on the actual location of the vehicle (based on gps or other location measurements while the vehicle is moving) or based on a route plan in a navigation system.
- VIN Vehicle Identification Number
- accident patterns and damage and injury and driver behavior patterns are stored. These patterns relate sequences of sensors reading or calculations based on sensor readings and information about the vehicle to accident detection. Damage patterns relate sensors reading and vehicle specific information to a damage assessment. Injury patterns relate sensors reading and vehicle specific information to assessment of driver and passenger injury. Driver patterns relate to driver safe operation of a vehicle.
- All vehicles are different: they have different options, they are exposed to different road conditions, and weather, they are used for different purposes, they are maintained differently and they are driven more or less frequently and have different drivers and most importantly, they have different weights. All of these factors can contribute to how much an insurance claim will be for any accident and also how quickly the vehicle can be repaired or an injury treated. In addition any of these pieces of information can also give clues to whether a resulting claim is wholly or partly fraudulent. For example, for a front end collision of a new vehicle, it should not be necessary to repaint the trunk, because the paint is not faded, and it would be easy to match the existing paint. If a car has a side impact airbag, and the car is impacted on the side, then injuries will potentially be far less than if the airbag was not available. However the damage estimate will be far greater due to the airbag deployment.
- Addition information that may be recorded on-board a vehicle and included with an accident report and/or added to the on-board vehicle database could be the following car history items:
- Patterns must be established that enable: the detection of an accident occurrence from onboard sensor data; assessment of the type and severity of damage/injury in a vehicle accident; and for driving behavior.
- the data is used to relate areas and times to the hazard of driving a given road segment.
- the central database in an embodiment, is initially created from historic records concerning accidents such as police accident reports and insurance adjuster on-site reports. These type of reports typically would not have on-board sensor data associated with an accident, but rather would have a written description of damage and the location of damage.
- the patterns for detection of an accident event for damage assessment and for injury assessment are designed to be relative to sensor data and no sensor data may be initially available, then a transfer function or correlation between the verbal description in accident reports and anticipated sensor response needs to be made.
- An accident report may document that there was an impact to a right front fender when another vehicle collided with the subject vehicle. It might further be documented that the subject vehicle was stationary and the colliding vehicle was moving at 20 kph (12mph). If the right front fender was damaged, but not the front bumper, an angle of impact of 45 degrees from the long axis of the vehicle might be inferred. The angle of impact could also be inferred from images of the damage that are taken at the accident scene.
- verbal descriptions and inferences from accident reports must be translated into a physical units to derive the accident, damage and injury patterns based initially on accident reports, but later to be based on sensor output. For example, given the relative speed of impact (velocity of impacting vehicle and the velocity of the impacted vehicle - for head on collision), and the weight of each vehicle, force of impact can be calculated. Given the force and direction of impact, then the maximum acceleration on impact (that would be detected by an accelerometer on a vehicle) could be approximately calculated. If the accident reports are standardized, then descriptions of accident specifics could be read electronically (if the report is in digital format) or by searching for keywords (if paper reports are optically character recognized).
- an initial accident detection pattern could simply be the observation that an airbag has deployed.
- the database could be a collection of remote databases that are linked and indexed together. So information about vehicle specifics could be in one database in Detroit, while accident event information could be in one of several other databases in other locations.
- New data is collected from vehicle on-board sensors and from external feeds continually, in an embodiment. At given time intervals the data for the last time period is stored and the older data is thrown out. Alternatively the data is stored in a memory stack of a set size where new data is added to top of the stack and the oldest data (at the bottom of the stack) is thrown out. At intervals which could correspond to the sample interval or multiples of the sampling rate, an accident patterns is looked for. If an accident patterns is detected, indicating an accident has occurred during the time interval, then the sampling rate may be increased to acquire more data per time period, and/or other sensor data, previously not being recorded, may be recorded.
- the end of the accident event in an embodiment, is defined when the vehicle is stationary. Once the accident is over, the stored data is analyzed to detect damage and injury patterns. Analysis is performed and if accident and/or injury patterns are detected, then the location and estimated damage and injury associated with these patterns is recorded.
- information about the accident is displayed on an infotainment screen in the vehicle or on an authorized portable device.
- the raw data and/or the sensor analysis is transferred to a server via the communication network (wireless, or otherwise) for inclusion into the central database and for future determination of accident, damage and injury patterns.
- FIG. 2 illustrates and embodiment of a vehicle on-board system.
- Realtime time series data is acquired from many sensor on-board the vehicle 202.
- the analysis software is comparing the sensor data feeds to either an accident pattern 210 or a driver pattern 212 in search of a correlation. If an accident pattern is detected this triggers recording of detail information 214 a a search for damage and injury patterns within the data 216. If a pattern is detected, then analysis is performed concerning the extent of damage or injury and the location of damage or injury 222 and this information along with the underlying data is transmitted to interested parties 218.
- a driver pattern is detected 212, a instance of the pattern is recorded 220 for later transmission 218 to, for example an insurance company, for analysis.
- Patterns are the vehicle on-board sensor output and other external environmental information observed during an accident. Patterns may be expressed as polynomial equation; they may be a threshold constant or upper and lower range for a specific sensor; they may be based on frequency and/or amplitude analysis of a single type or multiple types of sensors or they could be a statistical mean value for one or more sensor outputs or environmental factors. Patterns will change over time as more data is added, more sophisticated analysis is performed or more sensor types are available for on-board measurement. Patterns for one type of vehicle may be entirely different than for another type of vehicle. This may be due to different sensor suites being available or different physical attributes of the vehicle.
- patterns can be inferred from historic accident reports and/or vehicle on-board sensor data.
- the following tasks comprise steps to determine patterns initially based on accident reports:
- an accident impact could be inferred when a rapid deceleration is detected either by accelerometer measurements or change in speed measurements.
- Location, and relative speed of an impact can be inferred based on 3 component acceleration.
- a side impact can be inferred when a side airbag is deployed.
- Figure 3 shows one method of how to initially construct a spatially referenced database, to be used to predict insurance risk or risk of driving along a particular route or along certain transportation segments during a given time of day or time of week, based on existing historical information.
- a database of historical information is needed in order to determine baseline insurance premiums or risk and also to determine what type of information is available that may be relevant to insurance risk or driving risk.
- the development of the database assumes no strong correlation between any parameter and risk. For example an individual may consistently drive over the speed limit, but yet still be a safe driver - therefore, at least on an individual level, fast driving may not have a strong relationship to insurance risk, however as a whole, drivers in general may be a larger risk if they drive fast.
- the attribution used for insurance rating will be easier to deal with if it is consistent throughout the entire rating area. To accommodate this, it may be necessary to approximate a parameter stored in the database with input from a related parameter. For example, from the previous paragraph, you may wish to store accident occurrences associated with each road segment. If accident reports are not available for an area of interest but traffic flow information is, you may be able to infer that while traffic stops or slows way down that this is caused by an accident. This could then be reflected as an accident occurrence. This inferred accident occurrence could further be reflected in the metadata as the source for the accident count and an indication that the count is less reliable than an actual accident count. Another means of getting the proxy is the road quality, like road maintenance, and quality of the road surface type.
- the first step 302 is to find sources of historical information that potentially can be used singly or in tandem with other parameters to predict insurance and driving risk.
- the sources of information may vary locally, but it will be necessary to combine or map 306 the information from different sources that represent the same parameter into a single database field.
- Any model or predictive function could be greatly influenced by information that is acquired in real-time or near real time from drivers. This information could comprise things such as speed of travel, braking, engine function, acceleration, route taken and many others. If this information is readily available, it will influence the design of the predictive database. Therefore sources of pertinent real time information need to be identified 304.
- the database schema or design can then be created 308. All parameters to be stored in the database will be geographically referenced 314 with respect to an underlying GIS database 312 of the transportation network. Certain parameter (for example a speed limit) may also be temporally referenced.
- Figure 2 shows an example of how disparate information is combined into a single layer in the risk database.
- the example is given for accident reports but the technique also applies to any type of attribution.
- accident reports initially come from local police departments and/or directly from insurers, the format of the information and availability varies between departments or companies. For example, one department will have available accident reports that are
- the probability of an accident will increase with increased traffic density and/or due to inclement weather.
- This information may be available 310 with incoming accident reports or may be available via other sources such from a weather service which then can be related to an accident incident via location and time.
- the probably of an accident may increase based on the time. For example the probability of an accident most likely increases at 2 AM (2:00) on New Year's day as opposed to any other day at the same time. Therefore any form of attribution that can be associated with an incident should be added 312 so that it can be analyzed to see if there is any correlation with risk.
- the granularity of associated information will vary. For example if a traffic flow was associated with a particular accident and that traffic flow information was acquired from a Traffic Messaging Channel (TMC), this information may not be associated with the exact location of the accident and therefore may be suspect.
- TMC Traffic Messaging Channel
- the quality of the associated attribution for accident reports needs to be documented as metadata 314.
- accident reports would come from historical data such as police reports, however, this could be supplanted by real time information coming from vehicle sensors. For example, if an insurance subscriber allowed access to the insurer for output from car sensors, an accident incident could be recorded at the gps location of the vehicle when there was signal indicating that the air-bag was deployed. Once again the source of the report or parameter should be included as part of the metadata and be used as a measure of quality. Other driving telemetry obtaining devices which may be installed on the vehicle (perhaps at the behest of the insurance company) would be used to obtain additional pertinent information.
- the system can incorporate or utilize additional functionality as further described in U.S. Patent Application titled "SYSTEM AND METHOD FOR USE OF PATTERN RECOGN ITION I N ASSESSING OR MON ITORI NG VEHICLE STATUS OR OPERATOR DRIVING BEHAVIOR”; Application No. 13/679,722, filed November 16, 2012, herein incorporated by reference.
- the vehicle on-board database is specific to that vehicle. In order to keep this data current and geographically relevant, periodic syncing of the on-board vehicle database needs to occur with the central database repository.
- the server After a update has completed or if a new one is desired, then the server performs a query to determine all pieces of information that have changed since the last update and assigns transaction IDs to each database replace or insert operation that needs to happen.
- the query to determine what updates need to be performed are based on the date of the last successful complete update (or associated metadata), the vehicle VIN number and any alterations to the vehicle systems and the location and/or destination of the vehicle. Geographic information may be overwritten or simply added to the database depending on how much on-board storage is available. Update of geographically referenced data such as the location and contact information of approved repair establishments, occurs when the GPS or other location sensor on-board the vehicle determines the vehicle is out of a zone where this information is already stored. Alternatively, the information could be stored previous to a trip being planned.
- a single 3 component accelerometer is used as the sole sensor for accident detection, damage and injury assessment.
- the weight of the vehicle is known.
- the acceleration of the vehicle is measured continuously, for example, at a frequency of 100Hz. Acceleration measurements at each sampling interval are placed on top of the memory stack. After every sampling period, using the last successive acceleration measurements over a preset time interval and the vehicle weight, a running mean force vector and a mean force duration (impulse or momentum change) and a peak force vector are calculated.
- the accident detection pattern is defined as either when the peak-force exceeds a preset threshold or the amplitude of the mean-force vector exceeds a threshold value for a set amount of time. If one of these conditions occurs, then an accident is declared in progress.
- the thresholds for force and duration are configurable based on type of vehicle and weight of vehicle and historical information. If the same threshold were used for a many ton tractor trailer and a 1.5 ton smart car, you would get many false positive accident detection events for the tractor trailer.
- damage assessment occurs by comparing how much the thresholds were exceeded and the directionality of the force vector and comparison to historical records that indicate costs and parts and potential injuries and treatment costs associated with this type of force/duration and direction (this is the damage and injury assessment parameter).
- the raw data is uploaded to the central database and once actual figures for repair are input into the central database, this information becomes part of the historical database and the accident and damage patterns can be refined based on this new input.
- FNOL The First Notice of Loss
- Velocity of vehicle prior to accident include speed limit for the road as optional value to be added / or when not availa ble as unknown value
- VI N Unit can read VI N from vehicle if exposed in OBD; TOMS TDR file provide policy and vehicle details including the VI N)
- the vehicle on-board processor could send an accident report directly via SMS.
- incidents are evaluated based on the quantity and quality of information available and also the extent over which the information is available.
- the goal is to create a risk index or indices based on one or more of the type of incidents recorded related to elements of the transportation network.
- the quality of the information will influence the predictive model. It is well known that ice formation on a road is a function of temperature, humidity and barometric pressure. However if the weather conditions in an accident report are based on the general weather conditions for the region from a weather report, this data will not take into account, subtle weather variations that may be available from in-car sensors. A difference of a degree in temperature could make the difference between ice and no ice.
- initial assumptions need to be made to come up with a working predictive function 406. For example, initial weighting or correlation values might need to be assigned to the input variables. An educated guess may be that the number of pot holes in a road is about half as important to risk as the number of drunk driving arrests.
- an iterative process 410 is used to converge on a reasonable predictive model. This is done by modifying the weighting of input parameters slightly 412, then rerunning the new predictive function and observing the correlation statistics until a optimal correlation is arrived at.
- the input for a model may need to be parameterized in such a way as it can be used into a model.
- An example of parameterization would be to characterize incidents into a grouping. For example, it may be desirable to collectively refer to accidents counts falling into a range of 1-10 accidents per year as a "low” accident count and have "medium” and "high” counts as well.
- the assumption is made that insurance or driving risk for driving on a particular transportation element is directly correlated to the number of accidents reported on that element over a set time period. Therefore the risk database could simply contain accident incidents that are related to individual transportation segments. If available, additional attribution that may be recorded with accident incidents are, for example, direction of travel, time of day, date, and weather variables.
- insured drivers have agreed to have their driving habits monitored. If the person is applying for insurance, an initial insurance premium could be based partially on the area of residence and some average of accident risk within a geographic radius of the residence. Alternatively or after an initial rate is applied, the weekly habits (or longer duration) of the driver could be monitored. By monitoring when and where the driver has been, then, for example, it could be determined all the transportation elements the driver has traversed for a given rating interval and how many times they have been traversed.
- this embodiment would comprise assembling an accident incident database and linking accidents incidents to transportation elements 502. If any additional information is available such as the time of accident, the severity of the accident, the weather or pavement conditions, this should be included as associated attribution. Based on the incident information, an accident count could then be developed 504 which in its simplest form would be the average number of accidents that occur on each transportation element over a given time period. If other attribution is available, then the accident count could be further subdivided based by separating data, for example, for a given time of day or time of week, thus having multiple accident counts per transportation element. If severity information was available, then accident incidents could be weighted in the accident count, for example, an accident with a fatality could be counted as 10 times a minor accident.
- GIS geographic information
- Yet another embodiment of the present invention is a method to reduce driver / insurance risk utilizing one of the above described risk and driver habit databases and monitoring of a driver activities and habits in real-time.
- the system utilizes a navigation device located in a vehicle.
- the navigation device is either in communication with a risk database and a driving habits database or the databases are stored within the navigation device.
- the navigation device can be integral to the vehicle, a stand-alone device or software implemented on a computer, smartphone or tablet device. Generally location is determined by a GPS which is part of the navigation device.
- the system pre-determines routes that the driver in question has historically taken. It further determines the propensity of the driver to deviate from safe driving habits such as driving faster than the speed limit or swerving in the other lane or using a mobile phone (as determined from blue-tooth usage for example).
- the system determines whether the driver is driving a historical route, for example, driving towards work at a given time of day, or alternatively if a driver has input a route 606 to a new destination. If a route is being taken, the system next looks for real-time information from external sources of information 608 - for example traffic counts, accident reports or reports of lane closures. In addition, weather information along the route could also be acquired. Next, the travel time and risk assessment along the anticipated route is calculated by the navigation device. Alternate routes are also calculated taking into account the real-time information.
- this information can be displayed to the driver and a selection can be presented to route via the safer or faster route 618. If the safer route is selected 620, then the navigation system can either add an indication into the driver habit database, that the advice was taken or this can be transmitted to a server where insurance rates are determined. This information can then be used to affect insurance rates 616.
- Figure 7 depicts an embodiment of the present invention showing compilation of geo- referenced hazard information which can subsequently be displayed to vehicle drivers or which can be viewed on a map view showing transportation routes.
- Accident reports are compiled that are referenced to street addresses 702 or to map coordinates 706.
- the addresses are geocoded to get a location in map coordinates 720.
- Located accident reports are then associated with a transportation segment such as a road segment or intersection 708. Any associated environmental conditions are added to the database which are referenced to the time and place of the accident 710. Any pertinent additional data is also associated with the accident 712.
- metadata with respect to the quality of information is added to be used to remove outdated or poor quality data as more and better accident information comes it.
- the present invention may be conveniently implemented using one or more conventional general purpose or specialized digital computers or microprocessors programmed according to the teachings of the present disclosure, or a portable device (e.g., a smartphone, tablet computer, computer or other device), equipped with a data collection and assessment environment, including one or more data collection devices (e.g., accelerometers, GPS) or where the portable device are connected to the data collection devices that are remote to the portable device, that are connected via wired or wireless means.
- data collection devices e.g., accelerometers, GPS
- Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those skilled in the software art.
- the present invention includes a computer program product which is a non-transitory storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the processes of the present invention.
- the storage medium can include, but is not limited to, any type of disk including floppy disks, optical discs, DVD, CD- ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAM5, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
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