Method and system for risk modeling in autonomous vehicles

1. A method for adaptive risk modeling for a vehicle, the method comprising:

with sensor systems associated with the vehicle, generating:

a comparative model that compares vehicle operation to human driving operation in a set of driving scenarios,

a mix model characterizing operation of the vehicle in mixed-traffic driving scenarios,

a sensor-surrounding model characterizing surroundings of the autonomous vehicle;

with a supplementary data extraction system, generating:

a geography-dependent model characterizing geographic location-specific acceptable driving behaviors;

with a security diagnostic system associated with the vehicle, generating a security risk model characterizing security risks of the vehicle;

in response to an identified driving mission of the vehicle, generating values of: a comparative parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with the comparative model, the mix model, the sensor-surrounding model, the geography-dependent model, and the security risk model;

upon generating values, generating a risk analysis upon processing values of the comparative parameter, the mix model parameter, the surrounding risk parameter, the geographic operation parameter, and the security risk parameter with a rule-based algorithm; and

implementing a response action associated with control of the vehicle, based upon the risk analysis.

2. The method of claim 1 , wherein implementing the response action comprises providing the risk analysis to an entity that can perform vehicle-specific modifications of vehicle hardware based on the risk analysis.

3. The method of claim 1 , wherein implementing the response action comprises evaluating a second vehicle based on the risk analysis to generate a risk score associated with the second vehicle.

4. The method of claim 3 , wherein the second vehicle is operable under human power.

5. A method for adaptive risk modeling for a vehicle, comprising:

evaluating behavioral risk features of the vehicle according to a comparative model;

evaluating mixed traffic features of the vehicle from a mix model;

determining geography-dependent behavioral features associated with a geography-dependent model;

determining a surrounding risk parameter upon evaluating sensor systems of the vehicle and environmental conditions surrounding the vehicle with a sensor-surrounding model;

determining a security threat parameter for the vehicle with a security risk model;

transforming outputs of the comparative model, the mix model, the geography-dependent model, the sensor-surrounding model, and the security risk model, with an exposure parameter, into a risk analysis; and

implementing a response action associated with the vehicle based upon the risk analysis.

6. The method of claim 5 , wherein evaluating behavioral risk features comprises evaluating stress on vehicle subsystems.

7. The method of claim 6 , wherein evaluating stress on vehicle subsystems comprises evaluating forces incurred by vehicle mechanical subsystems during vehicle operation.

8. The method of claim 6 , further comprising determining a damage valuation metric based on the stress on vehicle subsystems.

9. The method of claim 8 , wherein implementing the response action comprises generating a maintenance alert based on the damage valuation metric, and providing the maintenance alert to an entity associated with the vehicle.

10. The method of claim 8 , wherein implementing the response action comprises performing vehicle-specific hardware modifications based on the risk analysis.

11. The method of claim 5 , wherein evaluating behavioral risk features comprises evaluating property damage associated with vehicle operation during driving maneuvers according to the comparative model.

12. The method of claim 5 , wherein evaluating mixed traffic features of the vehicle comprises evaluating vehicle operation within mixed traffic involving human-powerable transportation modes.

13. The method of claim 5 , wherein evaluating mixed traffic features comprises determining a number of negative incidents over a time period.

14. The method of claim 5 , wherein evaluating mixed traffic features comprises determining a number of negative incidents per mile driven.

15. The method of claim 5 , wherein determining geography-dependent behavioral features comprises assessing nearby object types at an optical sensor of the vehicle, and wherein transforming outputs into the risk analysis is based on the nearby object types.

16. The method of claim 5 , wherein the vehicle is operable by a human driver.

17. The method of claim 5 , wherein implementing the response action comprises evaluating a second vehicle based on the risk analysis to generate a risk score associated with the second vehicle.

18. The method of claim 17 , wherein the second vehicle is operable by a human.

19. The method of claim 5 , wherein implementing the response action comprises facilitating insurance processing based on the risk analysis.

20. The method of claim 5 , wherein transforming outputs into the risk analysis comprises: in response to an identified driving mission of the vehicle, generating values of: a comparative parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with the comparative model, the mix model, the sensor-surrounding model, the geography-dependent model, and the security risk model; and generating a risk analysis upon processing values of the comparative parameter, the mix model parameter, the surrounding risk parameter, the geographic operation parameter, and the security risk parameter with a rule-based algorithm.