Accident risk model determination using autonomous vehicle operating data

• US 9,852,475 B1
• Filed: 05/15/2015
• Issued: 12/26/2017
• 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): Wong, Eric T
• Assistant Examiner(s): Bradley, Joshua D.
• Application Number:
  US14/713,214
• Time in Patent Office: 956 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:

generating, by one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies, test data regarding results of virtual testing within the virtual test environment in which responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data are simulated;

receiving, by one or more processors, effectiveness information regarding the autonomous or semi-autonomous vehicle technology, the effectiveness information including both (i) actual accident data associated with vehicles having the autonomous or semi-autonomous vehicle technology and (ii) the test data associated with the autonomous or semi-autonomous vehicle technology;

determining, by one or more processors, an indication of reliability of the autonomous or semi-autonomous vehicle technology based at least in part upon compatibility of a version of or an update to computer-readable instructions involved in implementation of part or all of the autonomous or semi-autonomous vehicle technology with one or more versions of the at least one additional autonomous or semi-autonomous vehicle technologies tested;

determining, by one or more processors, an accident risk model based upon the received effectiveness information and the determined indication of reliability;

determining, by one or more processors, an insurance policy for a vehicle equipped with the autonomous or semi-autonomous vehicle technology based at least in part upon the accident risk model; and

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

2. The computer-implemented method of claim 1, wherein the accident risk model is associated with a likelihood that vehicles having the autonomous or semi-autonomous vehicle technology will be involved in vehicle accidents.

3. The computer-implemented method of claim 1, wherein the accident risk model comprises a data structure containing entries associated with at least one of (1) the autonomous or semi-autonomous vehicle technology or (2) a likelihood of a vehicle accident.

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

storing, by a non-transient computer-readable medium, the accident risk model;

receiving, by one or more processors, a request to determine the insurance policy for the vehicle; and

accessing, by one or more processors, the accident risk model based upon the received request.

5. 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.

6. The computer-implemented method of claim 1, wherein determining the insurance policy includes calculating at least one of the following based upon the autonomous or semi-autonomous vehicle technology and the accident risk model:

an automobile insurance premium, a discount, or a reward.

7. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology is related to at least one of the following:

driver alertness monitoring;

driver responsiveness monitoring;

pedestrian detection;

artificial intelligence;

a back-up system;

a navigation system;

a positioning system;

a security system;

an anti-hacking measure;

a theft prevention system;

or

remote vehicle location determination.

8. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology is related to at least one of the following:

a point of impact;

a type of road;

a time of day;

a weather condition;

a type of a trip;

a length of a trip;

a vehicle style;

a vehicle-to-vehicle communication;

or

a vehicle-to-infrastructure communication.

9. The computer-implemented method of claim 1, wherein causing information regarding all or a portion of the determined insurance policy for the vehicle to be presented to the customer includes causing to be presented on the display a cost of automobile insurance coverage.

10. The computer-implemented method of claim 1, wherein determining the accident risk model includes determining at least one risk level associated with the autonomous or semi-autonomous vehicle technology based upon observed responses of the autonomous or semi-autonomous vehicle technology in other vehicles.

11. The computer-implemented method of claim 1, wherein determining the insurance policy for the vehicle includes at least one of generating a new insurance policy associated with the vehicle or updating an existing insurance policy associated with the vehicle.

12. The computer-implemented method of claim 1, wherein the accident risk model further accounts for an effect of one or more of the following on the effectiveness information:

weather, road type, or vehicle type.

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

one or more processors;

one or more communication modules adapted to communicate data;

one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies to generate test data regarding results of virtual testing within the virtual test environment in which responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data are simulated, and wherein the test results are 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 effectiveness information regarding the autonomous or semi-autonomous vehicle technology, the effectiveness information including both (i) actual accident data associated with vehicles having the autonomous or semi-autonomous vehicle technology and (ii) the test data associated with the autonomous or semi-autonomous vehicle technology;

determine an indication of reliability of the autonomous or semi-autonomous vehicle technology based at least in part upon compatibility of a version of or an update to computer-readable instructions involved in implementation of part or all of the autonomous or semi-autonomous vehicle technology with one or more versions of the at least one additional autonomous or semi-autonomous vehicle technologies tested;

determine an accident risk model based upon the received effectiveness information and the determined indication of reliability;

determine an insurance policy for a vehicle equipped with the autonomous or semi-autonomous vehicle technology based at least in part upon the accident risk model; and

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

14. The computer system of claim 13, wherein the accident risk model is associated with a likelihood that vehicles having the autonomous or semi-autonomous vehicle technology will be involved in vehicle accidents.

15. The computer system of claim 13, wherein the accident risk model comprises a data structure containing entries associated with at least one of (1) the autonomous or semi-autonomous vehicle technology or (2) a likelihood of a vehicle accident.

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

store the accident risk model;

receive, via the one or more communication modules, a request to determine the insurance policy for the vehicle; and

access the accident risk model based upon the received request.

17. A tangible, non-transitory computer-readable medium storing 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:

generate, using one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies, test data regarding results of virtual testing within the virtual test environment in which responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data are simulated;

receive effectiveness information regarding the autonomous or semi-autonomous vehicle technology, the effectiveness information including both (i) actual accident data associated with vehicles having the autonomous or semi-autonomous vehicle technology and (ii) the test data associated with the autonomous or semi-autonomous vehicle technology;

determine an indication of reliability of the autonomous or semi-autonomous vehicle technology based at least in part upon compatibility of a version of or an update to computer-readable instructions involved in implementation of part or all of the autonomous or semi-autonomous vehicle technology with one or more versions of the at least one additional autonomous or semi-autonomous vehicle technologies tested;

determine an accident risk model based upon the received effectiveness information and the determined indication of reliability;

determine an insurance policy for a vehicle equipped with the autonomous or semi-autonomous vehicle technology based at least in part upon the accident risk model; and

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

18. The tangible, non-transitory computer-readable medium of claim 17, wherein the accident risk model is associated with a likelihood that vehicles having the autonomous or semi-autonomous vehicle technology will be involved in vehicle accidents.

19. The tangible, non-transitory computer-readable medium of claim 17, wherein the accident risk model comprises a data structure containing entries associated with at least one of (1) the autonomous or semi-autonomous vehicle technology or (2) a likelihood of a vehicle accident.

20. The tangible, non-transitory computer-readable medium of claim 17, further comprising executable instructions further cause the computer system to:

store the accident risk model;

receive, via one or more communication modules, a request to determine the insurance policy for the vehicle; and

access the accident risk model based upon the received request.