WO2018226222A1

WO2018226222A1 - Drug-based driver impairment detection

Info

Publication number: WO2018226222A1
Application number: PCT/US2017/036373
Authority: WO
Inventors: Jeffrey Allen Greenberg; Devinder Singh Kochhar; Kwaku O. PARKAH-ASANTE; Ali Hassani; David Melcher; Jeffrey Brian YEUNG; Kenneth Michael Mayer; Yifan Chen
Original assignee: Ford Global Technologies, Llc
Priority date: 2017-06-07
Filing date: 2017-06-07
Publication date: 2018-12-13

Abstract

A system including a computer with a processor that is programmed to receive user biometric data during operation of a vehicle, and select one or more drug-specific physiological markers from the biometric data based on a user's drug use profile. The processor is further programmed to actuate a vehicle component upon determining, based on a combination of the one or more drug-specific physiological markers and vehicle operating data, that a risk threshold is exceeded.

Description

DRUG-BASED DRIVER IMPAIRMENT DETECTION

BACKGROUND

[0001] Impairment, e.g., a lack of alertness, slowed reflexes, dulled senses, etc., of a vehicle user may cause accidents with other vehicles, pedestrians, etc. For example, user impairments can be caused by consumption of chemical substances, e.g., drugs. Consuming chemical substances may cause drowsiness, visual impairment, etc. It may be challenging to detect vehicle user impairment caused by drug's consumption.

BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Figure 1 is a diagram showing a vehicle system for detecting operator drug impairment.

[0003] Figure 2 is a flowchart of an exemplary process for determining a risk classifier to predict user drug consumption.

[0004] Figure 3 is a flowchart of an exemplary process to detect a vehicle user under the influence of drugs.

DETAILED DESCRIPTION

INTRODUCTION

[0005] Disclosed herein is a system including a computer with a processor that is programmed to receive user biometric data during operation of a vehicle, and select one or more drug-specific physiological markers from the biometric data based on a user' s drug use profile. The processor is further programmed to actuate a vehicle component upon determining, based on a combination of the one or more drug-specific physiological markers and vehicle operating data, that a risk threshold is exceeded.

[0006] The one or more physiological markers may include at least one of a heart rate, a blood pressure value, a reaction time, a pupillary response, a skin temperature, and muscle tremors.

[0007] The processor may be further programmed to receive the biometric data from an implantable biomedical device. [0008] The processor may be further programmed to receive the user drug consumption profile from a remote computer.

[0009] The processor may be further programmed to select at least one drug-specific physiological marker as one of the one or more drug-specific physiological markers that changes based on user drug consumption profile.

[0010] The processor may be further programmed to select at least one drug-specific physiological marker as one of the one or more drug-specific physiological markers that has a relationship to driving impairment.

[0011] The processor may be further programmed to determine that the risk threshold is exceeded based vehicle operating data including a time-to-collision, speed variations, average proximity to other road users, and a number of unexpected lane departures.

[0012] The processor may be further programmed to determine that the risk threshold is exceeded by determining a first risk indicator based on the vehicle operating data, determining a second risk indicator based on the selected one or more physiological markers, and determining that the risk threshold is exceeded based on the first and the second risk indicator.

[0013] Actuating the vehicle component may further include operating the vehicle in an autonomous mode.

[0014] The processor may be further programmed to determine that the risk threshold is exceeded based on one or more risk classifiers.

[0015] The system may further include a second computer that includes a processor programmed to determine the one or more risk classifiers by receiving, from a plurality of vehicles, user biometric data and vehicle operating data, and determining the risk classifiers based on the received user biometric data and the received vehicle operating data.

[0016] Further disclosed herein is a method that includes receiving user biometric data during operation of a vehicle, and selecting one or more drug-specific physiological markers from the biometric data based on a user's drug use profile. The method further includes actuating a vehicle component, upon determining, based on a combination of the one or more drug-specific physiological markers and vehicle operating data, that a risk threshold is exceeded. [0017] The one or more physiological markers may include at least one of a heart rate, a blood pressure value, a reaction time, a pupillary response, a skin temperature, and muscle tremors.

[0018] The method may further include receiving the biometric data from an implantable biomedical device.

[0019] The method may further include selecting at least one drug-specific physiological marker as one of the one or more drug-specific physiological markers that changes based on user drug consumption profile.

[0020] The method may further include selecting at least one drug- specific physiological marker as one of the one or more drug-specific physiological markers that has a relationship to driving impairment.

[0021] The method may further include determining that the risk threshold is exceeded based on vehicle operating data including a time-to-collision, speed variations, average proximity to other road users, and a number of unexpected lane departures.

[0022] Determining that the risk threshold is exceeded may further include determining a first risk indicator based on the vehicle operating data, determining a second risk indicator based on the selected one or more physiological markers, and determining that the risk threshold is exceeded based on the first and the second risk indicator.

[0023] Actuating the vehicle component may further include operating the vehicle in an autonomous mode.

[0024] The method may further include determining that the risk threshold is exceeded based on one or more risk classifiers.

[0025] Further disclosed is a computing device programmed to execute the any of the above method steps. Yet further disclosed is a vehicle comprising the computing device.

[0026] Yet further disclosed is a computer program product, comprising a computer readable medium storing instructions executable by a computer processor, to execute any of the above method steps. EXEMPLARY SYSTEM ELEMENTS

[0027] Figure 1 illustrates a vehicle 100. The vehicle 100 may be powered in a variety of known ways, e.g., with an internal combustion engine, electric motor, etc. Although illustrated as a passenger car, the vehicle 100 may be another kind of powered (e.g., electric and/or internal combustion engine) vehicle such as a car, a truck, a sport utility vehicle, a crossover vehicle, a van, a minivan, etc. The vehicle 100 may include a computer 110, actuator(s) 120, sensor(s) 130, and a human machine interface (HMI 140). In some examples, as discussed below, the vehicle is an autonomous vehicle configured to operate in an autonomous (e.g., driverless) mode, a semi-autonomous mode, and/or a non-autonomous mode.

[0028] The computer 110 includes a processor and a memory such as are known. The memory includes one or more forms of computer-readable media, and stores instructions executable by the computer 110 for performing various operations, including as disclosed herein.

[0029] The computer 110 may include programming to operate one or more systems of the vehicle 100, e.g., land vehicle brakes, propulsion (e.g., one or more of an internal combustion engine, electric motor, etc.), steering, climate control, interior and/or exterior lights, etc. The computer 110 may operate the vehicle 100 in an autonomous mode, a semi-autonomous mode, or a non-autonomous mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle propulsion, braking, and steering are controlled by the computer 110; in a semi- autonomous mode the computer controls one or two of vehicle propulsion, braking, and steering; in a non- autonomous mode, a human operator controls the vehicle propulsion, braking, and steering.

[0030] The computer 110 may include or be communicatively coupled to, e.g., via a communications bus of the vehicle 100 as described further below, more than one processor, e.g., controllers or the like included in the vehicle 100 for monitoring and/or controlling various controllers of the vehicle 100, e.g., a powertrain controller, a brake controller, a steering controller, etc. The computer 110 is generally arranged for communications on a communication network of the vehicle 100, which can include a bus in the vehicle 100 such as a controller area network (CAN) or the like, and/or other wired and/or wireless mechanisms. [0031] Via the communication network of the vehicle 100, the computer 110 may transmit messages to various devices in the vehicle 100 and/or receive messages from the various devices, e.g., an actuator 120, an HMI 140, etc. Alternatively or additionally, in cases where the computer 110 actually comprises multiple devices, the vehicle communication network may be used for communications between devices represented as the computer 110 in this disclosure.

[0032] The actuators 120 of the vehicle 100 are implemented via circuits, chips, or other electronic and/or mechanical components that can actuate various vehicle subsystems in accordance with appropriate control signals, as is known. The actuators 120 may be used to control vehicle systems such as braking, acceleration, and/or steering of the vehicles 100.

[0033] In addition, the computer 110 may be configured for communicating through a vehicle-to-infrastructure (V-to-I) interface with other vehicles, and/or a remote computer 180 via a network 190. The network 190 represents one or more mechanisms by which the computer 110 and the remote computer 180 may communicate with each other, and may be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks (e.g., using one or more of cellular, Bluetooth, IEEE 802.11, etc.), dedicated short range communications (DSRC), local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services.

[0034] The HMI 140 may be configured to receive user input, e.g., during operation of the vehicle 100. Moreover, an HMI 140 may be configured to present information to the user. Thus, the HMI 140 is typically located in a passenger cabin of the vehicle 100. For example, the HMI 140 may provide information to the user including an indication of vehicle 100 user impairment, an activation of vehicle 100 autonomous mode based on vehicle 100 user impairment, etc. [0035] The sensors 130 may include a variety of devices known to provide data to the computer 110. For example, the sensors 130 may include Light Detection And Ranging (LIDAR) sensor(s) 130 disposed on a top, a pillar, etc. of the vehicle 100 that provide relative locations, sizes, and shapes of other vehicles and/or objects surrounding the vehicle 100. As another example, one or more radar sensors 130 fixed to vehicle 100 bumpers may provide locations of the second vehicles 101 travelling in front, side, and/or rear of the vehicle 100, relative to the location of the vehicle 100. The sensors 130 may further alternatively or additionally include camera sensor(s) 130, e.g. front view, side view, etc., providing images from an area around the vehicle 100. For example, the computer 110 may be programmed to receive image data from the camera sensor(s) 130 and to implement image processing techniques to detect lane markings, traffic signs, and/or other objects such as other vehicles. As another example, the computer 110 may be programmed to determine whether a distance to another vehicle is less than a predetermined threshold, whether an unexpected lane departure occurred, etc. The computer 110 may receive object data from, e.g., camera sensor 130, and operate the vehicle 100 in an autonomous and/or semi-autonomous mode based at least in part on the received object data.

[0036] The sensors 130 may include a Global Positioning Sensor 130 (GPS). Based on data received from the GPS sensor 130, the computer 110 may determine geographical location coordinates, movement direction, speed, etc., of the vehicle 100. The sensors 130 may include acceleration sensors 130 providing longitudinal and/or lateral acceleration of the vehicle 100.

[0037] The sensors 130 may include a camera sensor 130 with a field of view including a vehicle 100 interior. For example, the field of view of the camera sensor 130 may include a vehicle 100 user. The computer 110 may be programmed to determine user biometric data such as a posture, face direction, pupil diameter, pupillary response rate, etc. of the user based on image data received from the camera sensor 130. The computer 110 may be programmed to receive biometric data from other sensors 130, e.g., temperature sensor 130 included in a user seat, air sensor, microphone, etc. Additionally, vehicle 100 user devices may operate as sensor 130. For example, a wearable device 160 may provide user biometric data such as user heart rate. A transdermal patch 150 that is typically used for drug delivery may include sensors to determine various biometric data such as blood content of a chemical substance, etc., As another example, an implantable biomedical device such as a miniaturized robot implanted in user's body (e.g. inside blood vessels), a device implanted under the skin, etc. may provide biometric data of the user.

[0038] The biometric data, in the context of present disclosure, is data about a physical state or attribute of a user and may include user physiological markers such as a posture, eye status (e.g., open, close, etc.), pupil diameter, heart rate, breadth rate, blood pressure value, reaction time, pupillary response, skin temperature, muscle tremors, etc. The term "physiological marker" (used herein interchangeably with the terms "biological marker" and "biomarker") typically refers to a measurable indicator of some biological state or condition, e.g., a pulse rate, a respiration rate, a body temperature, pupil dilation, a concentration of a chemical in the bloodstream, etc.

[0039] A user posture may include location coordinates of user hands, curvature of user spine including neck curvature, angle of user spine relative to vehicle 100 floor, etc. The face direction may include three-dimensional coordinates of a line of sight extending from the user face.

[0040] The biometric data may include vehicle 100 user personal information or profile such as age, height, weight, medical record, etc. The computer 110 may be programmed to receive user profile from the remote computer 180, e.g., via the communication network 190. The medical record in the context of present disclosure, may include user health condition including any diagnosed physiological and/or mental condition, etc. Additionally, the medical record may include information including prescribed and/or over-the-counter drugs. A drug consumption profile may include drug dosage (e.g., 200milligram (mg) per capsule), consumption (e.g., 3 capsules/day), etc. Additionally or alternatively, the medical record may include purchase history including over-the-counter drugs, and/or prescribed drugs.

[0041] Drugs may have side effect(s). A side effect is an effect, whether therapeutic or adverse, that is secondary to an intended effect of a drug, typically the purpose for which a drug is prescribed and/or consumed. Side effects may include drowsiness, cognitive impairment, vision impairment, dizziness, weakness, etc. Side effects may be indicated by one or more physiological markers. For example, a dizziness side effect may be related to reduction of blood pressure which is a physiological marker. Relationships may exist between side effects and (i) physiological markers. A relationship, as the term is used herein, may include an increase, decrease, deviation rate, etc., in one data value based in a change of another data value. For example, a relationship may include a 10% decrease in user's blood pressure based on a dosage of 200mg/day of a drug. Data including side effects and related physiological markers may be stored in a memory of the computer 110, e.g., received from the remote computer 180. Additionally, a relationship may exist between side effects and vehicle 100 operating data received via vehicle 100 sensors 130. For example, a relationship may include between a drowsiness side effect and a 30% increase of a number of unexpected lane departures.

[0042] In one example, a side effect caused by a first drug may include (and/or may be related to) an increase of blood pressure marker. For example, an increase of more than a threshold, e.g., 20%, compared to an average blood pressure marker associated with the user may indicate a consumption of the drug.

[0043] In another example, a cognitive impairment side-effect may be caused by a second drug, e.g., may be related to a reaction time physiological marker and/or vehicle 100 operating data, e.g., number of unexpected lane changes. The reaction time marker of a vehicle 100 user in the context of this disclosure refers to a time from occurrence of an event until the vehicle 100 user reacts to the occurred event. For example, a time from a pedestrian crossing a road within a predetermined distance of the vehicle 100 until the user actuates a vehicle 100 brake pedal. An increase of a user reaction time marker compared to a typically expected value may be an indicator of a user cognitive impairment. Additionally or alternatively, a number of unexpected lane changes greater than a threshold, e.g., 5 times per minute, may indicate a consumption of the second drug. Side effects may be caused due to interaction of drugs, e.g., the first and the second drug. An interaction side effects side effect may be caused due to simultaneous consumption of multiple drugs. For example, an occurrence of an interaction side effect related to the first and the second drugs may indicate a simultaneous consumption of the first and the second drugs.

[0044] The computer 110 may be programmed to receive user biometric data during operation of the vehicle 100, select one or more drug-specific (i.e., specific to one or more drugs) physiological markers from the biometric data based on a user's drug use profile. The computer 110 may be further programmed to actuate a vehicle 100 component, e.g., the HMI 140, upon determining, based on a combination of the one or more selected drug-specific physiological markers and vehicle operating data, that a risk threshold is exceeded.

[0045] The computer 110 may be programmed to activate a vehicle 100 autonomous mode upon determining that the risk threshold is exceeded. Additionally or alternatively, the computer 110 may be programmed to send a message including, e.g., vehicle identifier, etc., to the remote computer 180, upon determining that the risk threshold is exceeded.

[0046] The risk as that term is used herein is a value, e.g., specified by a number, indicating a likelihood of a deviation of and/or an amount of deviation of a vehicle 100 user performance from an expected user performance caused by vehicle 100 user impairment. The expected user performance, in the context of present disclosure, may refer to vehicle 100 operation including controlling of speed, steering, braking, etc. A deviation of expected user performance may be measured according a change in vehicle speed, steering braking, etc., e.g., a lane departure, sudden braking, sudden acceleration, extremely low or high speeds (e.g., more than 25% above or below an established speed limit), etc., may indicate a deviation of expected user performance.

[0047] As discussed below, the risk may be determined based on a classifier. An impact may include collision with another vehicle and/or pedestrian, unexpected lane departure, etc. In one example, the risk may be assigned to one of a plurality of discrete categories, such as "low", "medium", "high", and "imminent" risk. A risk level may be correlated to a likelihood of vehicle 100 impact. For example, a "high" level of risk compared to a "low" level of risk may indicate a higher likelihood of vehicle 100 impact. Upon detecting a risk above a threshold, the computer 110 may actuate the vehicle 100 actuators 120 to cause an action such as stopping the vehicle 100, activating a vehicle 100 autonomous mode, etc., if the risk is "high", i.e., greater than a "medium" risk threshold. In another example, the risk may be defined as a numerical percentage value between 0% and 100%. For example, the computer 110 may actuate the vehicle 100 actuators 120 to cause an action when the risk, e.g. 60%, is greater than a risk threshold, e.g., 50%.

[0048] The computer 110 may be programmed to score the drugs based on the side effects associated with the drugs and select the physiological markers based on the scores associated with the drugs. The score as that term is used herein is a value, e.g., specified by a number between 0 and 10, indicating a relevance (or relationship) of a side effect to driving impairment. For example, a score of 1 may indicate a lower relevance of a side effect such as a skin rash to driving capability. In another example, a score of 9 may indicate a higher relevance of a side effect such as vision impairment, drowsiness, etc. to driving capability. The computer 110 may be programmed to select a physiological marker of a drug upon determining that the score of the respective drug exceeds a predetermined score threshold value, e.g., 5. For example, the computer 110 may be programmed to select a side effect among multiple side effects upon determining that only the score of the selected side effect exceeds the predetermined score threshold value. Additionally or alternatively, the score may be assigned to one of a plurality of discrete categories, such as "low", "medium", "high" relevance. For example, the computer 110 may be programmed to select a side effect when its score is greater than a "medium" score threshold. The computer 110 may be programmed to receive a score, e.g., "medium", associated with a side effect from the remote computer 180. In one example, the remote computer 180 may be programmed to determine a score for each drug based on data received from, e.g., a drug manufacturer computer, etc., and/or data including physiological markers and sensor 130 data from the vehicles 100 and/or user mobile devices 170.

[0049] The computer 110 may be programmed to select at least one drug-specific physiological marker among one or more drug-specific physiological markers that changes based on user's drug consumption. In one example, the computer 110 may be programmed to determine based on user specific medical record that a drug consumption may not cause a side effect by the respective vehicle 100 user. In another example, the computer 110 may be programmed to select a physiological marker upon determining that a consumption of a drug may cause a change in the selected marker such as a change of pupillary response rate. The computer 110 may be programmed to select a physiological marker and/or operating data upon determining that a change of the respective marker or operating data exceeds a change threshold, e.g., 20%.

[0050] The computer 110 may be programmed to select a drug-specific physiological marker that has a relationship to driving impairment. For example, the computer 110 may be programmed to select a physiological marker such as a skin temperature which may have a relationship to driving impairment, e.g., a fever may be also accompanied by drowsiness, cognitive impairment, etc., whereas a skin rash may not be related to driving impairment.

[0051] As discussed above, the computer 110 may be programmed to determine the risk based on selected markers and/or vehicle 100 operating data. The computer 110 may be programmed to determine the risk based on vehicle operating data (or driving performance data) including a time-to-collision, speed variations, average proximity to other road users, and a number of unexpected lane departures.

[0052] As discussed above, the computer 110 may be programmed to determine whether a side effect of a drug has occurred based on the received data. On the other hand, another side effect may not be determinable due to lack of a measurable physiological marker and/or operating data that is related to the side effect. In one example, the computer 110 may be programmed to determine that a side effect of a drug has occurred based on determining that another side effect of the drug has occurred. For example, the computer 110 may lack data indicating a physiological marker that is related to an anxiety side effect. However, the computer 110 may be programmed to determine that the user consumed the drug based on a physiological marker such as an increasing blood pressure. Thus, the computer 110 may be programmed to determine that the user also suffers under anxiety side effect based on determining the consumption of the drug. In other words, the computer 110 may be programmed to determine the risk based on the anxiety side effect, although the computer 110 may lack an ability to directly measure anxiety due to a lack of data specifying a physiological marker that is directly related to anxiety. "Directly" in this context means that there is a defined relationship between the side effect and a measurable physiological marker and/or vehicle 100 operating data.

[0053] The computer 110 may be programmed to determine based on an interaction side effect that a user has consumed multiple drugs. For example, simultaneous (or close-in-time) consumption of a first and a second drug may cause an interaction side effect. The first drug may additionally cause a first side effect. The second drug may additionally cause a second side effect. The computer 110 may be programmed to determine based on physiological marker(s) and/or vehicle operating data whether an interaction side effect has occurred. Thus, the computer 110 may be programmed to determine that first and the second side effects of the first and the second drugs occurred based on determining that the interaction side effect occurred, although the computer 110 may lack an ability to determine directly whether the first and the second side effects occurred.

[0054] The computer 110 may be programmed to receive data defining relationships between drugs and side effects from a remote computer 180. Additionally or alternatively, the computer 110 may be programmed to determine the relationships based on data received from vehicle 100 sensors 130, the wearable device 160, the transdermal patch 150, etc. For example, the computer 110 may be programmed to determine relationships between (i) vehicle 100 sensor 130 data and physiological markers, and (ii) drug's consumption based on the received data, using artificial intelligence techniques.

[0055] A side effect may be related to a combination of physiological marker(s) and vehicle operating data. The computer 110 may be programmed to determine the risk by determining a first risk indicator based on the vehicle 100 operating data and a second risk indicator based on the selected physiological markers. The computer 110 then determines the risk based on the first and the second risk indicators.

[0056] For example, the computer 110 may be programmed to determine that the user is visually impaired upon determining that pupillary response time of the user exceeds a pupillary response threshold, e.g., 1 second, and/or an angle between a user face direction and a vehicle 100 forward direction exceeds an angle threshold of 45 degrees. In one example, the computer 110 may be programmed to determine the first risk indicator (a number between 0 and 10) based on the pupillary response time and the second risk indicator (a number between 0 and 10) based on the user face direction. For example, the computer 110 may determine the risk based on an accumulation (sum) of the first risk indicator and the second risk indicator and actuate a vehicle 100 component upon determining that the accumulated risk exceeds a threshold of 4. Thus, advantageously, the computer may determine the risk based on inputs (physiological markers, operating data, etc.) that have different ranges, units, and thresholds. The computer 110 may be programmed based on artificial intelligence techniques to optimize accumulation operations, e.g., sum operation in example above, to determine the risk based on a combination of physiological markers and/or vehicle 100 operating data.

[0057] In another example, the computer 110 may be programmed to determine the risk based on a risk classifier. The risk classifier may include a mathematical operation such as aiXi + Ά2Χ2 + biYi +b2Y2. In the foregoing example expression, Xi, X₂, etc., represent physiological markers. Further, Yi, Y₂, etc., represent vehicle 100 sensor 130 data such as speed, acceleration, etc. The parameters ai, a₂, etc., an bi, b2, etc. may be optimized to define the risk classifier. In one example, the computer 110 may be programmed to determine optimized parameters ai, a₂, etc., an bi, b2, etc. using artificial intelligence and/or other known optimization techniques such as genetic algorithms. The computer 110 may be programmed to perform an action such as actuating a vehicle 100 component upon determining that the risk calculated based on the risk classifier exceeds a risk threshold. For example, the computer 110 may be programmed to cause an action assigned to a risk level, e.g., as shown in Table 1. The computer 110 may activate a vehicle 100 semi-autonomous mode, e.g., controlling a vehicle 100 steering operation, upon determining a medium risk. Upon determining a high risk, the computer 110 may activate a vehicle 100 autonomous mode to navigate the vehicle 100 to a vehicle 100 destination. Upon determining an imminent risk, the computer 110 may activate a vehicle 100 autonomous mode to navigate the vehicle 100 to a road side, e.g., nearest possible road side where the vehicle 100 can stop, and stop the vehicle 100. Risk Action

Low No action

medium Activate semi-autonomous mode

High Activate autonomous mode

Imminent Navigate to side of road and stop the vehicle

Table 1

[0058] For example, the remote computer 180 may receive data from multiple vehicles 100 via the communication network 190 and determine the risk classifier(s) using optimization techniques, artificial intelligence techniques, etc. The received data may include the physiological markers, vehicles 100 operating data, users' medical profile, etc. Additionally or alternatively, the remote computer 180 may be programmed to determine relationships of drugs and side effects and/or relationships of side effects and (i) vehicle data and (ii) physical markers, based on received data. For example, the remote computer 180 may receive data from multiple vehicles 100 and determine side effects and/or associated physiological markers/operating data based on the received data. A "classifier," as the term is used herein, may include mathematical and/or logical operations based on input values such as physiological markers, vehicle 100 operating data, medical profiles, drug consumption profile, etc. Such classifiers are then used by the vehicle 100 computers 110 to determine a risk of vehicle 100 accident posed by a user under drug influence. In other words, the remote computer 180 may be further programmed to determine the relationships based on the received data from multiple users and vehicles 100.

PROCESSING

[0059] Figure 2 is a flowchart of an exemplary process 200 for determining a classifier including relationships of user physiological markers, vehicle 100 sensor 130 data, and user drug consumption. For example, the remote computer 180 may be programmed to execute blocks of the process 200.

[0060] The process 200 begins in a block 210, in which the remote computer 180 receives biometric data of multiple users. For example, the remote computer 180 may be programmed to receive biometric data of multiple users in multiple vehicles 100, e.g., via users' mobile devices 170 and the communication network 190. The biometric data may include user profile including age, weight, prescribed drugs, drug purchase history, etc.

[0061] Next, in a block 220, the remote computer 180 receives sensor 130 data from a plurality of vehicle 100. The sensor 130 data may include speed, lateral acceleration, longitudinal acceleration, etc. Additionally, the computer 180 may be programmed to receive vehicle 100 user biometric data. Thus, the remote computer 180 may be programmed to associate user biometric data to a respective vehicle 100.

[0062] Next, in a block 230, the remote computer determines risk classifiers. The remote computer 180 may be programmed to determine relationships between the drugs side effects and user physiological markers and/or between the drugs side effects and the vehicle 100 operating data. The computer 180 may be further programmed to determine classifiers to determine the risk based on the received physiological markers and/or operating data. An example of a risk classifier was discussed above, and is an example of possible output from the block 230.

[0063] Next, in a block 240, the remote computer 180 transmits the risk classifiers to the vehicles 100. For example, the remote computer 180 may be programmed to transmit periodically, e.g., daily, updated risk classifiers to the vehicle 100 via the communication network 190. Additionally or alternatively, the remote computer 180 may be programmed to transmit risk classifiers to a vehicle 100 based on a request for risk classifiers received from the vehicle 100. Following the block 240, the process 200 ends, or alternatively returns to the block 210, although not shown in Figure 3.

[0064] Figure 4 is a flowchart of an exemplary process for a vehicle to detect driving under influence of a drug or drugs. For example, the vehicle 100 computer 110 may be programmed to execute blocks of the process 300.

[0065] The process 300 begins in a block 310, in which the vehicle 100 computer 110 receives biometric data of the vehicle 100 user.

[0066] Next, in a block 320, the computer 110 receives vehicle 100 sensor 130 data such as speed, lateral acceleration, longitudinal acceleration, etc. [0067] Next, in a block 330, the computer 110 receives physiological markers from the wearable device 160, a transdermal patch 150, an implantable device, etc. and/or from a vehicle 100 sensor 130 such as a camera sensor 130 with a field of view including the user, a temperature sensor 130 included in a vehicle seat.

[0068] Next, in a block 340, the computer 110 receives risk classifiers, drug side effects data, etc., from the remote computer 180.

[0069] Next, in a block 350, the computer 110 selects one or more physiological markers. For example, the computer 110 may be programmed to select the physiological markers based on a score of the drugs that the user consumes, e.g., upon determining that a score of a drug exceeds a score threshold, and selects the physiological markers of the selected drugs. Additionally or alternatively, the computer 110 may be programmed to select the physiological markers that have a relationship to driving impairment. Additionally or alternatively, the computer 110 may be programmed to select the physiological markers that change based on drug' s consumption. Additionally or alternatively, the computer 110 may be programmed to select vehicle 100 operating data, e.g., number of unexpected lane departures based on a drowsiness side effect of a drug consumed by the vehicle 100 user. Additionally or alternatively, the computer 110 may be programmed to select one or more physiological markers based on vehicle 100 operating data. For example, the computer 110 may be programmed to select one or more physiological marker upon determining that a number of unexpected lane departures exceeds a predetermined threshold.

[0070] Next, in a block 360, the computer 110 determines a risk, e.g., based on the received data, the selected physiological marker, and the received risk classifiers. For example, the computer 110 may be programmed to determine the risk based on a classifier that as explained above includes a mathematical operation based on inputs including values for physiological markers, biometric data, and vehicle 100 operating data.

[0071] Next, in a decision block 370, the computer 110 determines whether the determined risk exceeds a risk threshold. If the computer 110 determines that the determined risk exceeds the threshold, then the process 300 proceeds to a block 380; otherwise the process 300 ends, or alternatively returns to the block 310. [0072] In the block 380, the computer 110 causes an action. For example, the computer 110 may activate vehicle 100 actuators 120 based on an action assigned to a risk level, e.g., as shown in Table 1 above.

[0073] Following the block 380, the process 300 ends, or alternatively returns to the block 310, although not shown in Figure 4.

[0074] The article "a" modifying a noun should be understood as meaning one or more unless stated otherwise, or context requires otherwise. The phrase "based on" encompasses being partly or entirely based on.

[0075] Computing devices as discussed herein generally each include instructions executable by one or more computing devices such as those identified above, and for carrying out blocks or steps of processes described above. Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer-readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media. A file in the computing device is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc.

[0076] A computer-readable medium includes any medium that participates in providing data (e.g., instructions), which may be read by a computer. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, etc. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes a main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH, an EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.

[0077] With regard to the media, processes, systems, methods, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of systems and/or processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the disclosed subject matter.

[0078] Accordingly, it is to be understood that the present disclosure, including the above description and the accompanying figures and below claims, is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to claims appended hereto and/or included in a non-provisional patent application based hereon, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the disclosed subject matter is capable of modification and variation.

Claims

CLAIMS What is claimed is:

1. A system, comprising a computer including a processor programmed to: receive user biometric data during operation of a vehicle;

select one or more drug-specific physiological markers from the biometric data based on a user's drug use profile; and

upon determining, based on a combination of the one or more

drug-specific physiological markers and vehicle operating data, that a risk threshold is exceeded, actuate a vehicle component.

2. The system of claim 1, wherein the one or more physiological markers include at least one of a heart rate, a blood pressure value, a reaction time, a pupillary response, a skin temperature, and muscle tremors.

3. The system of claim 1, the processor further programmed to receive the biometric data from an implantable biomedical device.

4. The system of claim 1, the processor further programmed to receive user drug consumption profile from a remote computer.

5. The system of claim 1, the processor further programmed to select at least one drug-specific physiological marker as one of the one or more drug-specific physiological markers that changes based on user drug consumption profile.

6. The system of claim 1, the processor further programmed to select at least one drug-specific physiological marker as one of the one or more drug-specific physiological markers that has a relationship to driving impairment.

7. The system of claim 1, the processor further programmed to determine that the risk threshold is exceeded based vehicle operating data including a time-to-collision, speed variations, average proximity to other road users, and a number of unexpected lane departures.

8. The system of claim 1, the processor further programmed to determine that the risk threshold is exceeded by:

determining a first risk indicator based on the vehicle operating data; determining a second risk indicator based on the selected one or more physiological markers; and

determining that the risk threshold is exceeded based on the first and the second risk indicator.

9. The system of claim 1, wherein actuating the vehicle component further includes operating the vehicle in an autonomous mode.

10. The system of claim 1, the processor further programmed to determine that the risk threshold is exceeded based on one or more risk classifiers.

11. The system of claim 10, further comprising a second computer that includes a second processor programmed to determine the one or more risk classifiers by:

receiving, from a plurality of vehicles, user biometric data and vehicle operating data; and

determining the risk classifiers based on the received user biometric data and the vehicle operating data.

12. A method, comprising:

receiving user biometric data during operation of a vehicle;

selecting one or more drug-specific physiological markers from the received user biometric data based on a user's drug use profile; and

upon determining, based on a combination of the one or more

drug-specific physiological markers and vehicle operating data, that a risk threshold is exceeded, actuating a vehicle component.

13. The method of claim 12, wherein the one or more physiological markers include at least one of a heart rate, a blood pressure value, a reaction time, a pupillary response, a skin temperature, and muscle tremors.

14. The method of claim 12, further comprising receiving the received user biometric data from an implantable biomedical device.

15. The method of claim 12, further comprising selecting at least one drug-specific physiological marker from the one or more drug-specific physiological markers that changes based on user drug consumption profile.

16. The method of claim 12, further comprising selecting at least one drug-specific physiological marker from the one or more drug-specific physiological markers that has a relationship to driving impairment.

17. The method of claim 12, further comprising determining that the risk threshold is exceeded based on vehicle operating data including a

time-to-collision, speed variations, average proximity to other road users, and a number of unexpected lane departures.

18. The method of claim 12, wherein determining that the risk threshold is exceeded further includes:

determining a first risk indicator based on the vehicle operating data; determining a second risk indicator based on the selected physiological markers; and

19. The method of claim 12, wherein actuating the vehicle component further includes operating the vehicle in an autonomous mode.

20. The method of claim 12, further comprising determining that the risk threshold is exceeded based on one or more risk classifiers.