System and method for using human driving patterns to manage speed control for autonomous vehicles

Patent metadata

Field	Value
Publication number	US-11983008-B2
Application number	US-202217654224-A
Country	US
Kind code	B2
Filing date	Mar 9, 2022
Priority date	Sep 7, 2017
Publication date	May 14, 2024
Grant date	May 14, 2024

Abstract

A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.

First claim

What is claimed is:

1. A method, comprising:
receiving a vehicle control command prior to controlling an autonomous vehicle to perform the vehicle control command;
comparing the vehicle control command to standards of driving behavior and validating or modifying the vehicle control command according to the comparing; and
causing the autonomous vehicle to perform the vehicle control command according to the validated or modified vehicle control command.

2. The method of claim 1 , wherein the standards of driving behavior are determined by training a reinforcement learning process comprising training by simulation to generate data from the standards of driving behavior that is compared to data corresponding to human driving behaviors during the simulation.

3. The method of claim 1 , wherein the standards of driving behavior are determined by training a reinforcement learning process comprising training using on-the-road data from the standards of driving behavior that is compared to data corresponding to human driving behaviors captured by sensors of the autonomous vehicle.

4. The method of claim 1 , wherein the standards of driving behavior are determined by training a neural network.

5. The method of claim 1 , further comprising:
updating one or more driving parameters in response to the comparing.

6. The method of claim 5 , wherein the driving parameters comprise:
a speed parameter,
a braking parameter, or
a steering angle parameter of the autonomous vehicle.

7. The method of claim 5 , further comprising:
determining a current state of the autonomous vehicle and determining a deviation between the current state of the autonomous vehicle and a first state corresponding to the standards of driving behavior, wherein the driving parameters are modified based on the deviation between the current state of the autonomous vehicle and the first state.

8. The method of claim 7 , wherein the deviation is larger than a first deviation between the current state of the autonomous vehicle and data corresponding to human driving behaviors.

9. An apparatus, comprising:
at least one processor; and
at least one memory including executable instructions that, when executed, cause the at least one processor to perform operations comprising:
receiving a vehicle control command prior to controlling an autonomous vehicle to perform the vehicle control command;
comparing the vehicle control command to standards of driving behavior and validating or modifying the vehicle control command according to the comparing; and
causing the autonomous vehicle to perform the vehicle control command according to the validated or modified vehicle control command.

10. The apparatus of claim 9 , wherein the standards of driving behavior are determined by training a reinforcement learning process comprising training by simulation to generate data from the standards of driving behavior that is compared to data corresponding to human driving behaviors during the simulation.

11. The apparatus of claim 9 , wherein the standards of driving behavior are determined by training a reinforcement learning process comprising training using on-the-road data from the standards of driving behavior that is compared to data corresponding to human driving behaviors captured by sensors of the autonomous vehicle.

12. The apparatus of claim 9 , wherein the standards of driving behavior are determined by training a neural network or generating a rules set.

13. The apparatus of claim 9 , wherein the modifying the vehicle control command modifies driving parameters in the vehicle control command.

14. The apparatus of claim 13 , wherein the driving parameters comprise:
a speed parameter,
a braking parameter, or
a steering angle parameter of the autonomous vehicle.

15. The apparatus of claim 14 , wherein the executable instructions further cause the at least one processor to perform operations comprising:
determining a current state of the autonomous vehicle and determining a deviation between the current state of the autonomous vehicle and a first state corresponding to the standards of driving behavior, wherein the driving parameters are modified based on the deviation between the current state of the autonomous vehicle and the first state.

16. The apparatus of claim 15 , wherein the deviation is larger than a first deviation between the current state of the autonomous vehicle and data corresponding to human driving behaviors.

17. A non-transitory machine-readable storage medium including instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
receiving a vehicle control command prior to controlling an autonomous vehicle to perform the vehicle control command;
comparing the vehicle control command to standards of driving behavior and validating or modifying the vehicle control command according to the comparing; and
causing the autonomous vehicle to perform the vehicle control command according to the validated or modified vehicle control command.

18. The non-transitory machine-readable storage medium of claim 17 , wherein the standards of driving behavior are determined by training a reinforcement learning process comprising training by simulation to generate data from the standards of driving behavior that is compared to data corresponding to human driving behaviors during the simulation.

19. The non-transitory machine-readable storage medium of claim 17 , wherein the standards of driving behavior are determined by training a reinforcement learning process comprising training using on-the-road data from the standards of driving behavior that is compared to data corresponding to human driving behaviors captured by sensors of the autonomous vehicle.

20. The non-transitory machine-readable storage medium of claim 17 , wherein the standards of driving behavior are determined by training a neural network.

Assignees

• Tusimple Inc

Inventors

• Lin Wutu
• Liu Liu
• Sun Xing
• Ma Kai-Chieh
• Xuan Zijie
• Zhao Yufei

Classifications

• B60W60/001 Primary

  Planning or execution of driving tasks · CPC title

• G05D1/0088 Primary

  characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00) · CPC title

• B60W40/09

  Driving style or behaviour · CPC title

• B60W2720/103

  Speed profile · CPC title

• B60W40/105 Primary

  Speed · CPC title