Accident risk model determination using autonomous vehicle operating data

• US 10,510,123 B1
• Filed: 11/08/2017
• Issued: 12/17/2019
• Est. Priority Date: 05/20/2014
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Konrardy, Blake, Christensen, Scott T., Hayward, Gregory, Farris, Scott
• Primary Examiner(s): Oyebisi, Ojo O
• Application Number:
  US15/806,784
• Time in Patent Office: 769 Days
• Field of Search: 705 4
• CPC Class Codes: B60Q 11/005 for lighting devices, e.g. indicating if lamps are burning or not
• CPC Class Codes: B60Q 9/00 Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00 , e.g. haptic signalling
• CPC Class Codes: B60R 2021/0027 Post collision measures, e.g. notifying emergency services
• CPC Class Codes: B60R 21/00 Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks safety belts or body harnesses in vehicles B60R22/00; devices, apparatus or methods for life-saving in general A62B; safety devices for propulsion unit control specially adapted for, or arranged in, vehicles B60K28/00; seats constructed to protect the occupant from the effect of abnormal g-forces, e.g. crash or safety seats, B60N2/42; energy-absorbing arrangements for hand wheels for steering vehicles B62D1/11; energy-absorbing arrangements for vehicle steering columns B62D1/19; harnessing in aircraft B64D25/00
• CPC Class Codes: B60W 2040/0818 Inactivity or incapacity of driver
• CPC Class Codes: B60W 2050/0095 Automatic control mode change
• CPC Class Codes: B60W 2050/0096 Control during transition between modes
• CPC Class Codes: B60W 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 15/931 of land vehicles
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 19/13 Receivers
• CPC Class Codes: G01S 19/48 by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
• CPC Class Codes: G01S 2013/9318 Controlling the steering
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06F 30/15 Vehicle, aircraft or watercraft design
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 20/0855 involving a third party
• CPC Class Codes: G06Q 40/04 : Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06Q 50/40 : Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G06V 20/597 : Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/08 : Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 : Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/085 : using electronic data carriers
• CPC Class Codes: G08B 21/06 : indicating a condition of sleep, e.g. anti-dozing alarms
• CPC Class Codes: G08B 25/08 : using communication transmission lines G08B13/19658, G08B21/0286, G08B25/016 take precedence
• CPC Class Codes: G08G 1/005 : including pedestrian guidance indicator
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 : where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/09675 : where a selection from the received information takes place in the vehicle
• CPC Class Codes: G08G 1/096758 : where no selection takes place on the transmitted or the received information
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096783 : where the origin of the information is a roadside individual element
• CPC Class Codes: G08G 1/096791 : where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/143 : inside the vehicles
• CPC Class Codes: G08G 1/147 : where the parking area is within an open public zone, e.g. city centre
• CPC Class Codes: G08G 1/164 : Centralised systems, e.g. external to vehicles
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: G08G 1/205 : Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/44 : for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• CPC Class Codes: H04W 4/46 : for vehicle-to-vehicle communication [V2V]
• CPC Class Codes: H04W 4/90 : Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]

Abstract

Methods and systems for evaluating the effectiveness of autonomous operation features of autonomous vehicles using an accident risk model are provided. According to certain aspects, an accident risk model may be determined using effectiveness information regarding autonomous operation features associated with a vehicle. The effectiveness information may indicate a likelihood of an accident for the vehicle and may include test data or actual loss data. Determining the likelihood of an accident may include determining risk factors for the features related to the ability of the features to make control decisions that successfully avoid accidents. The accident risk model may further include information regarding effectiveness of the features relative to location or operating conditions, as well as types and severity of accidents. The accident risk model may further be used to determine or adjust aspects of an insurance policy associated with an autonomous vehicle.

Claims

1. A computer-implemented method of evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, the method comprising:

implementing, by one or more processors, the autonomous or semi-autonomous vehicle technology within a virtual test environment configured to simultaneously test multiple autonomous or semi-autonomous vehicle technologies;

presenting, by the one or more processors, virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;

generating, by the one or more processors, test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;

generating, by the one or more processors, an accident risk model indicating one or more risk levels for vehicle accidents associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;

receiving, at the one or more processors, actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment;

adjusting, by the one or more processors, the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level;

identifying, by the one or more processors, a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and

generating or updating, by the one or more processors, an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.

2. The computer-implemented method of claim 1, wherein:

generating the test responses includes generating test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology; and

the one or more risk levels of the accident risk model are generated based in part upon compatibility of the test responses of the autonomous or semi-autonomous vehicle technology with the additional test responses of the other autonomous or semi-autonomous vehicle technology.

3. The computer-implemented method of claim 2, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.

4. The computer-implemented method of claim 1, wherein generating the accident risk model includes determining the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.

5. The computer-implemented method of claim 1, further comprising:

causing, by the one or more processors, information regarding all or a portion of the insurance policy to be presented to a customer associated with the customer vehicle via a display of a customer computing device for review.

6. The computer-implemented method of claim 1, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.

7. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology involves at least one of a vehicle self-braking functionality or a vehicle self-steering functionality.

8. The computer-implemented method of claim 1, wherein the operating conditions are associated with one or more of the following:

a road type, a time of day, or a weather condition.

9. A computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, comprising:

one or more processors;

one or more program memories coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to;

implement the autonomous or semi-autonomous vehicle technology within a virtual test environment configured to simultaneously test multiple autonomous or semi-autonomous vehicle technologies;

present virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;

generate test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;

generate an accident risk model indicating one or more risk levels for vehicle accidents associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;

receive actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment;

adjust the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level;

identify a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and

generate or update an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.

10. The computer system of claim 9, wherein:

the executable instructions that cause the computer system to generate the test responses cause the computer system to generate test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology; and

the one or more risk levels of the accident risk model are generated based in part upon compatibility of the test responses of the autonomous or semi-autonomous vehicle technology with the additional test responses of the other autonomous or semi-autonomous vehicle technology.

11. The computer system of claim 10, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.

12. The computer system of claim 9, wherein the executable instructions that cause the computer system to generate the accident risk model further cause the computer system to determine the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.

13. The computer system of claim 9, wherein the executable instructions further cause the computer system to:

communicate to a customer computing device, via a communication network, information regarding all or a portion of the insurance policy to be presented to a customer associated with the customer vehicle for review via a display of the customer computing device.

14. The computer system of claim 9, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.

15. A tangible, non-transitory computer-readable medium storing executable instructions for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology that, when executed by at least one processor of a computer system, cause the computer system to:

implement the autonomous or semi-autonomous vehicle technology within a virtual test environment configured to simultaneously test multiple autonomous or semi-autonomous vehicle technologies;

present virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;

generate test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;

generate an accident risk model indicating one or more risk levels for vehicle accidents associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;

receive actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment;

adjust the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level;

identify a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and

generate or update an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.

16. The tangible, non-transitory computer-readable medium of claim 15, wherein:

the executable instructions that cause the computer system to generate the test responses cause the computer system to generate test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology; and

the one or more risk levels of the accident risk model are generated based in part upon compatibility of the test responses of the autonomous or semi-autonomous vehicle technology with the additional test responses of the other autonomous or semi-autonomous vehicle technology.

17. The tangible, non-transitory computer-readable medium of claim 16, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.

18. The tangible, non-transitory computer-readable medium of claim 15, wherein the executable instructions that cause the computer system to generate the accident risk model further cause the computer system to determine the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.

19. The tangible, non-transitory computer-readable medium of claim 15, further storing executable instructions that, when executed by at least one processor of the computer system, cause the computer system to:

cause information regarding all or a portion of the insurance policy to be presented to a customer associated with the customer vehicle via a display of a customer computing device for review.

20. The tangible, non-transitory computer-readable medium of claim 15, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.