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

Patent metadata

Field	Value
Publication number	US-12554257-B2
Application number	US-202418631700-A
Country	US
Kind code	B2
Filing date	Apr 10, 2024
Priority date	Sep 7, 2017
Publication date	Feb 17, 2026
Grant date	Feb 17, 2026

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 vehicle operational data to simulated vehicle data and updating a driving behavior model according to the comparing; validating or modifying the vehicle control command based on the updated driving behavior model; 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 driving behavior model is trained by a reinforcement learning process comprising training by simulation to generate data that is compared to data corresponding to human driving behaviors during the simulation.

3 . The method of claim 1 , wherein the driving behavior model is trained by a reinforcement learning process comprising training by comparing data captured while the autonomous vehicle is in operation on a road to data corresponding to human driving behaviors captured by sensors of the autonomous vehicle.

4 . The method of claim 1 , wherein the comparing comprises determining a deviation between the vehicle operational data at time steps and the simulated vehicle data at corresponding time steps.

5 . The method of claim 3 , wherein updating the driving behavior model comprises performing the reinforcement learning process in iterations that modify parameters of the driving behavior model.

6 . The method of claim 1 , wherein the driving behavior model is based on driving parameters comprising:
a speed parameter, a braking parameter, or a steering angle parameter of the autonomous vehicle.

7 . The method of claim 5 , wherein the comparing comprises determining a deviation between the vehicle operational data and the simulated vehicle data relative to a threshold, wherein the vehicle operational data comprises a trajectory of the autonomous vehicle during a first iteration of the reinforcement learning process.

8 . The method of claim 1 , wherein the vehicle operational data is aggregated from data collected from a population of vehicles and drivers.

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 vehicle operational data to simulated vehicle data and updating a driving behavior model according to the comparing; validating or modifying the vehicle control command based on the updated driving behavior model; 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 driving behavior model is trained by a reinforcement learning process comprising training by simulation to generate data that is compared to data corresponding to human driving behaviors during the simulation.

11 . The apparatus of claim 9 , wherein the driving behavior model is trained by a reinforcement learning process comprising training by comparing data captured while the autonomous vehicle is in operation on a road to data corresponding to human driving behaviors captured by sensors of the autonomous vehicle.

12 . The apparatus of claim 9 , wherein the driving behavior model is trained to identify standards of driving behavior 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 behavior model is based on driving parameters comprising:
a speed parameter, a braking parameter, or a steering angle parameter of the autonomous vehicle.

15 . The apparatus of claim 10 , wherein updating the driving behavior model comprises performing the reinforcement learning process in iterations that modify parameters of the driving behavior model.

16 . The apparatus of claim 15 , wherein the vehicle operational data is aggregated from data collected from a population of vehicles and drivers.

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 vehicle operational data to simulated vehicle data and updating a driving behavior model according to the comparing; validating or modifying the vehicle control command based on the updated driving behavior model; 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 driving behavior model is trained by a reinforcement learning process comprising training by simulation to generate data 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 driving behavior model is trained by a reinforcement learning process comprising training by comparing data captured while the autonomous vehicle is in operation on a road 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 comparing comprises determining a deviation between the vehicle operational data at time steps and the simulated vehicle data at corresponding time steps.

Assignees

• Tusimple Inc
• Createai Inc

Inventors

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

Classifications

• G05D1/81

  Handing over between on-board automatic and on-board manual control · CPC title

• B60W2720/103

  Speed profile · CPC title

• B60W40/09

  Driving style or behaviour · CPC title

• B60W2050/0002

  Automatic control, details of type of controller or control system architecture · CPC title

• B60W30/09

  Taking automatic action to avoid collision, e.g. braking and steering · CPC title