Systems and methods for mitigating spoofing of vehicle features
Systems and methods for mitigating certain spoofing of vehicle features are disclosed herein. An example method can include determining input torque values obtained from a steering torque sensor associated with a steering wheel of a vehicle, wherein the input torque values are obtained over a period of time, determining road disturbances using a road disturbance model, applying a driver model that is indicative of human driver hands-on-wheel behaviors, determining when input torque values are indicative of a spoof or human interaction with the steering wheel using the input torque values, the road disturbance model, and the driver model, and executing a remediating measure when the input torque values are indicative of the spoof.
1. A method comprising:
determining input torque values obtained from a steering torque sensor associated with a steering input mechanism of a vehicle, wherein the input torque values are obtained over a period of time;
determining road disturbances using a road disturbance model;
determining an angle rate of change of the steering input mechanism over the period of time;
applying a driver model that is indicative of human driver hands-on-wheel behaviors;
determining when input torque values are indicative of a spoof or human interaction with the steering input mechanism using the input torque values, the road disturbance model, the angle rate of change, and the driver model; and
executing a remediating measure when the input torque values are indicative of the spoof.
2. The method according to claim 1 , further comprising generating the road disturbance model from sensor output obtained from a vehicle sensor platform that comprises at least one camera, the sensor output being images obtained by the at least one camera.
3. The method according to claim 1 , further comprising:
determining angle values of the steering input mechanism over the period of time, the angle values being used in determining when input torque values are indicative of the spoof or human interaction.
4. The method according to claim 1 , wherein the remediating measure includes displaying a warning message on a human-machine interface of the vehicle.
5. The method according to claim 1 , wherein the remediating measure includes causing an advanced driver assistance system of the vehicle to slow the vehicle or deactivate a fully or semi-autonomous mode of steering.
6. The method according to claim 1 , wherein the input torque values are indicative of the human interaction when the input torque values are quasi-random and the input torque values are indicative of the spoof when the input torque values are cyclical.
7. The method of claim 1 , further comprising:
filtering noise from the input torque values, the noise being inferred from the road disturbance model, the noise being created from road and/or environmental conditions.
8. The method according to claim 1 , further comprising determining and applying vehicle acceleration values over the period of time.
9. The method according to claim 1 , further comprising determining when the spoof is created by a device associated with the steering input mechanism.
10. A vehicle, comprising:
a steering wheel of the vehicle;
a steering torque sensor coupled to the steering wheel; and
an advance driver assistance system (ADAS), comprising a processor and memory, the processor executing instructions stored in the memory to:
determine input torque values obtained from the steering torque sensor associated with the steering wheel of the vehicle, wherein the input torque values are obtained over a period of time;
determine road disturbances using a road disturbance model;
determine an angle rate of change of the steering wheel over the period of time;
apply a driver model that is indicative of human driver hands-on-wheel behaviors;
determine when input torque values are indicative of a spoof or human interaction with the steering wheel using the input torque values, the road disturbance model, the angle rate of change, and the driver model; and
execute a remediating measure when the input torque values are indicative of the spoof.
11. The vehicle according to claim 10 , further comprising a vehicle sensor platform that comprises at least a camera, the ADAS being configured to generate the road disturbance model from images obtained from the camera, the ADAS using a modeling engine that applies image processing logic to detect road disturbances in the images.
12. The vehicle according to claim 10 , wherein the ADAS is configured to:
determine angle values of the steering wheel over the period of time, the angle values being used in determining when input torque values are indicative of the spoof or human interaction.
13. The vehicle according to claim 10 , wherein the remediating measure includes the ADAS causing a warning message to be displayed on a human-machine interface of the vehicle.
14. The vehicle according to claim 10 , wherein the remediating measure includes the ADAS slowing the vehicle or deactivate a fully or semi-autonomous mode of steering.
15. The vehicle according to claim 10 , wherein the input torque values are indicative of the human interaction when the input torque values are quasi-random and the input torque values are indicative of the spoof when the input torque values are cyclical.
16. The vehicle according to claim 10 , wherein the ADAS is configured to filter noise from the input torque values, the noise being inferred from the road disturbance model, the noise being created from road conditions.
17. The vehicle according to claim 10 , wherein the ADAS is configured to determine and apply vehicle acceleration values over the period of time.
18. The vehicle according to claim 10 , wherein the ADAS is configured to determine when the spoof is created by a device associated with the steering wheel.
19. A method comprising:
determining input torque values obtained from a steering torque sensor associated with a steering wheel of a vehicle, wherein the input torque values are obtained over a period of time;
applying a driver model that is indicative of human driver hands-on-wheel behaviors;
generating a road disturbance model from images obtained from a camera;
detecting, by an advance driver assistance system (ADAS) and using a modeling engine that applies image processing logic, road disturbances in the images;
determining when input torque values are indicative of a spoof or human interaction with the steering wheel using the input torque values, the road disturbance model, and the driver model; and
executing a remediating measure when the input torque values are indicative of the spoof.
20. The method according to claim 19 , wherein the remediating measure includes slowing the vehicle or deactivate a fully or semi-autonomous mode of steering.