Method and system for risk modeling in autonomous vehicles

• US 10,678,250 B2
• Filed: 06/05/2018
• Issued: 06/09/2020
• Est. Priority Date: 12/09/2016
• Status: Active Grant

• Current Assignee: Zendrive, Inc.
• Original Assignee: Zendrive, Inc.
• Inventors: Matus, Jonathan, Risbood, Pankaj
• Primary Examiner(s): Badii, Behrang
• Application Number:
  US16/000,675
• Publication Number:
  US 20180292835A1
• Time in Patent Office: 735 Days
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4046 Behavior, e.g. aggressive or erratic
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/00 Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/0098 Details of control systems ensuring comfort, safety or stability not otherwise provided for
• CPC Class Codes: B60W 50/082 Selecting or switching between different modes of propelling
• CPC Class Codes: B60W 60/00188 related to detected security violation of control systems, e.g. hacking of moving vehicle
• CPC Class Codes: B60W 60/0053 from vehicle to occupant
• CPC Class Codes: B60W 60/0059 Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: B64C 39/02 characterised by special use
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64D 11/0624 Arrangements of electrical connectors, e.g. for earphone, internet or electric supply
• CPC Class Codes: G05D 1/00 : Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots drive control systems specially adapted for autonomous road vehicles B60W60/00
• CPC Class Codes: G06F 18/00 : Pattern recognition
• CPC Class Codes: G06F 18/23 : Clustering techniques
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 40/20 : Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G08G 1/0112 : from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 5/00 : Traffic control systems for aircraft , e.g. air-traffic control [ATC]
• CPC Class Codes: G08G 5/003 : Flight plan management

Abstract

A method for adaptive risk modeling for an autonomous vehicle, the method comprising: retrieving parameters of an identified driving mission of the autonomous vehicle; in response to the parameters of the identified driving mission, generating values of: a comparative autonomous 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 a comparative autonomous model, a mix model, a sensor-surrounding model, a geography-dependent model, and a security risk model generated using sensor and supplementary data extraction systems associated with the autonomous vehicle; upon generating values, generating a risk analysis with a rule-based algorithm; and contemporaneously with execution of the identified driving mission, implementing a response action associated with control of the autonomous vehicle, based upon the risk analysis.

Claims

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.