Fully autonomous vehicle insurance pricing

• US 10,089,693 B1
• Filed: 05/15/2015
• Issued: 10/02/2018
• 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): Ebersman, Bruce I
• Assistant Examiner(s): Bradley, Joshua D.
• Application Number:
  US14/713,271
• Time in Patent Office: 1,236 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 determining risk associated with operation of fully autonomous vehicles are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including types and version of sensors, control systems, and software. This information may be used to determine a risk profile reflecting risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels may further be determined and used with the risk profile to determine a total risk level associated with operation of the vehicle by the autonomous operation features. The expected use levels may indicate expected vehicle use, as well as traffic, weather, or other conditions in which the vehicle is likely to operate. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.

Claims

1. A computer-implemented method for evaluating effectiveness of one or more autonomous operation features in controlling a vehicle, comprising:

generating, by one or more test units configured to simultaneously test a plurality of autonomous operation features, test results for the plurality of autonomous operation features including a plurality of versions of computer-readable instructions of at least one of the one or more autonomous operation features of the vehicle, wherein the test units generate the test results as control commands generated by the one or more autonomous operation features of the test units in response to test sensor data that simulates sensor data for an autonomous vehicle operating under a plurality of operating conditions within a virtual test environment;

receiving, at one or more processors, information regarding the one or more autonomous operation features of the vehicle under the plurality of operating conditions, the information identifying the one or more autonomous operation features of the vehicle, including an indication of a type and a current version of the plurality of versions of the at least one of the one or more autonomous operation features and an indication of a type and a version of an additional autonomous operation feature of the one or more autonomous operation features;

determining, by one or more processors, a risk profile associated with autonomous operation of the vehicle based upon the information regarding the one or more autonomous operation features and the test results generated by the one or more test units, wherein the risk profile includes a plurality of risk levels associated with autonomous operation of the vehicle under the plurality of operating conditions by the one or more autonomous operation features, including a plurality of risk levels associated with the plurality of versions of the at least one of the one or more autonomous operation features, wherein each of the plurality of risk levels is determined based upon an effectiveness of a respective version of the plurality of versions of the at least one of the one or more autonomous operation features in controlling an aspect of operation of the vehicle under a respective operating condition of the plurality of operating conditions, and wherein each of the plurality of risk levels is also associated with the additional autonomous operation feature;

determining, by one or more processors, a plurality of expected use levels of the vehicle, wherein the expected use levels are associated with the plurality of operating conditions and the current version of the plurality of versions of the at least one of the one or more autonomous operation features;

determining, by one or more processors, a total risk level associated with autonomous operation of the vehicle by the current version of the plurality of versions of the at least one of the one or more autonomous operation features and the additional autonomous operation feature based at least in part upon the determined risk profile and the determined plurality of expected use levels; and

causing, by one or more processors, one or more of the following actions to be performed based upon the determined total risk level;

adjusting an insurance policy associated with the vehicle, determining a coverage level associated with the insurance policy, presenting information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or presenting the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.

2. The method of claim 1, wherein the total risk level is determined without reference to factors relating to risks associated with a vehicle operator.

3. The method of claim 1, wherein:

the test results include responses of the test units to test inputs corresponding to test scenarios.

4. The method of claim 1, wherein the information regarding the one or more autonomous operation features of the vehicle is based upon (i) the test results for the one or more test units, each of the one or more test units corresponding to the one or more autonomous operation features, which test results include responses of the test units to test inputs corresponding to test scenarios, and (ii) actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features.

5. The method of claim 1, wherein the expected use levels include one or more of (i) expected autonomous operation levels of the vehicle, (ii) expected operation of the vehicle by a vehicle operator, or (iii) expected settings associated with the one or more autonomous operation features.

6. The method of claim 1, further comprising:

receiving, at one or more processors, a request for a quote of a premium associated with a vehicle insurance policy;

determining, by one or more processors, a premium associated with the vehicle insurance policy based upon the total risk level; and

presenting, by one or more processors, an option to purchase the vehicle insurance policy to a customer associated with the vehicle.

7. The method of claim 1, wherein the information regarding the one or more autonomous operation features includes one or more of the following with respect to each of the one or more autonomous operation features:

a type and version of the autonomous operation feature, an operation of the autonomous operation feature, a type and version of autonomous operation feature control software, or settings of the autonomous operation feature.

8. The method of claim 1, wherein:

receiving information regarding the one or more autonomous operation features of the vehicle further comprises;

receiving, at one or more processors, information regarding the vehicle;

determining, by one or more processors, types of the one or more autonomous operation features and types of one or more sensors installed in the vehicle based upon the information regarding the vehicle; and

determining, by one or more processors, the plurality of risk levels associated with autonomous operation of the vehicle, at least in part, based upon the sensors installed in the vehicle.

9. A computer system for evaluating effectiveness of one or more autonomous operation features in controlling a vehicle, comprising:

one or more processors;

one or more communication modules adapted to communicate data;

one or more test units configured to simultaneously test a plurality of autonomous operation features and to generate test results for the plurality of autonomous operation features including a plurality of versions of computer-readable instructions of at least one of the one or more autonomous operation features of the vehicle, wherein the test units generate the test results as control commands generated by the one or more autonomous operation features of the test units in response to test sensor data that simulates sensor data for an autonomous vehicle operating under a plurality of operating conditions within a virtual test environment, and wherein the test data is communicated to the one or more processors via the one or more communication modules; and

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

receive information regarding the one or more autonomous operation features of the vehicle under the plurality of operating conditions, the information identifying the one or more autonomous operation features of the vehicle, including an indication of a type and a current version of the plurality of versions of the at least one of the one or more autonomous operation features and an indication of a type and a version of an additional autonomous operation feature of the one or more autonomous operation features;

determine a risk profile associated with autonomous operation of the vehicle based upon the information regarding the one or more autonomous operation features and the test results generated by the one or more test units, wherein the risk profile includes a plurality of risk levels associated with autonomous operation of the vehicle under the plurality of operating conditions by the one or more autonomous operation features, including a plurality of risk levels associated with the plurality of versions of the at least one of the one or more autonomous operation features, wherein each of the plurality of risk levels is determined based upon an effectiveness of a respective version of the plurality of versions of the at least one of the one or more autonomous operation features in controlling an aspect of operation of the vehicle under a respective operating condition of the plurality of operating conditions, and wherein each of the plurality of risk levels is also associated with the additional autonomous operation feature;

determine a plurality of expected use levels of the vehicle, wherein the expected use levels are associated with the plurality of operating conditions and the current version of the plurality of versions of the at least one of the one or more autonomous operation features;

determine a total risk level associated with autonomous operation of the vehicle by the current version of the plurality of versions of the at least one of the one or more autonomous operation features and the additional autonomous operation feature based at least in part upon the determined risk profile and the determined plurality of expected use levels; and

cause one or more of the following actions to be performed based upon the determined total risk level;

adjusting an insurance policy associated with the vehicle, determining a coverage level associated with the insurance policy, presenting information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or presenting the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.

10. The computer system of claim 9, wherein the total risk level is determined without reference to factors relating to risks associated with a vehicle operator.

11. The computer system of claim 9, wherein:

the test results include responses of the test units to test inputs corresponding to test scenarios.

12. The computer system of claim 9, wherein the information regarding the one or more autonomous operation features of the vehicle is based upon (i) the test results for the one or more test units, each of the one or more test units corresponding to the one or more autonomous operation features, which test results include responses of the test units to test inputs corresponding to test scenarios, and (ii) actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features.

13. The computer system of claim 9, wherein the expected use levels include one or more of (i) expected autonomous operation levels of the vehicle, (ii) expected operation of the vehicle by a vehicle operator, or (iii) expected settings associated with the one or more autonomous operation features.

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

receive a request for a quote of a premium associated with a vehicle insurance policy;

determine a premium associated with the vehicle insurance policy based upon the total risk level; and

present an option to purchase the vehicle insurance policy to a customer associated with the vehicle.

15. The computer system of claim 9, wherein the information regarding the one or more autonomous operation features includes one or more of the following with respect to each of the one or more autonomous operation features:

a type and version of the autonomous operation feature, an operation of the autonomous operation feature, a type and version of autonomous operation feature control software, or settings of the autonomous operation feature.

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

receive information regarding the vehicle;

determine types of the one or more autonomous operation features and types of one or more sensors installed in the vehicle based upon the information regarding the vehicle; and

determine the plurality of risk levels associated with autonomous operation of the vehicle, at least in part, based upon the sensors installed in the vehicle.

17. A tangible, non-transitory computer-readable medium storing instructions for evaluating effectiveness of one or more autonomous operation features in controlling a vehicle that, when executed by at least one processor of a computer system, cause the computer system to:

generate test results for a plurality of autonomous operation features including a plurality of versions of computer-readable instructions of at least one of the one or more autonomous operation features of the vehicle using one or more test units configured to simultaneously test the plurality of autonomous operation features, wherein the test units generate the test results as control commands generated by the one or more autonomous operation features of the test units in response to test sensor data that simulates sensor data for an autonomous vehicle operating under a plurality of operating conditions within a virtual test environment;

receive information regarding the one or more autonomous operation features of the vehicle under the plurality of operating conditions, the information identifying the one or more autonomous operation features of the vehicle, including an indication of a type and a current version of the plurality of versions of the at least one of the one or more autonomous operation features and an indication of a type and a version of an additional autonomous operation feature of the one or more autonomous operation features;

determine a risk profile associated with autonomous operation of the vehicle based upon the information regarding the one or more autonomous operation features and the test results generated by the one or more test units, wherein the risk profile includes a plurality of risk levels associated with autonomous operation of the vehicle under the plurality of operating conditions by the one or more autonomous operation features, including a plurality of risk levels associated with the plurality of versions of the at least one of the one or more autonomous operation features, wherein each of the plurality of risk levels is determined based upon an effectiveness of a respective version of the plurality of versions of the at least one of the one or more autonomous operation features in controlling an aspect of operation of the vehicle under a respective operating condition of the plurality of operating conditions, and wherein each of the plurality of risk levels is also associated with the additional autonomous operation feature;

determine a plurality of expected use levels of the vehicle, wherein the expected use levels are associated with the plurality of operating conditions and the current version of the plurality of versions of the at least one of the one or more autonomous operation features;

determine a total risk level associated with autonomous operation of the vehicle by the current version of the plurality of versions of the at least one of the one or more autonomous operation features and the additional autonomous operation feature based at least in part upon the determined risk profile and the determined plurality of expected use levels; and

cause one or more of the following actions to be performed based upon the determined total risk level;

adjusting an insurance policy associated with the vehicle, determining a coverage level associated with the insurance policy, presenting information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or presenting the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.

18. The tangible, non-transitory computer-readable medium of claim 17, wherein the total risk level is determined without reference to factors relating to risks associated with a vehicle operator.

19. The tangible, non-transitory computer-readable medium of claim 17, wherein the information regarding the one or more autonomous operation features of the vehicle is based upon (i) the test results for the one or more test units, each of the one or more test units corresponding to the one or more autonomous operation features, which test results include responses of the test units to test inputs corresponding to test scenarios, and (ii) actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features.

20. The tangible, non-transitory computer-readable medium of claim 17, wherein the expected use levels include one or more of (i) expected autonomous operation levels of the vehicle, (ii) expected operation of the vehicle by a vehicle operator, or (iii) expected settings associated with the one or more autonomous operation features.