System and method for path planning of autonomous vehicles based on gradient

• US 10,710,592 B2
• Filed: 04/07/2017
• Issued: 07/14/2020
• Est. Priority Date: 04/07/2017
• Status: Active Grant

• Current Assignee: TuSimple, Inc. (TuSimple Holdings, Inc.)
• Original Assignee: TuSimple, Inc. (TuSimple Holdings, Inc.)
• Inventors: Lin, Wutu, Hou, Xiaodi
• Primary Examiner(s): Wallace, Donald J
• Application Number:
  US15/481,877
• Publication Number:
  US 20180292222A1
• Time in Patent Office: 1,194 Days
• CPC Class Codes: B60W 2050/0029 Mathematical model of the driver
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0214 in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G05D 1/0221 involving a learning process

Abstract

A system and method for path planning of autonomous vehicles based on gradient are disclosed. A particular embodiment includes: generating and scoring a first suggested trajectory for an autonomous vehicle; generating a trajectory gradient based on the first suggested trajectory; generating and scoring a second suggested trajectory for the autonomous vehicle, the second suggested trajectory being based on the first suggested trajectory and a human driving model; and outputting the second suggested trajectory if the score corresponding to the second suggested trajectory is within a score differential threshold relative to the score corresponding to the first suggested trajectory.

Claims

1. A system comprising:

a data processor;

a path planning module, executable by the data processor, the path planning module being configured to perform a path planning operation for autonomous vehicles based on gradient, the path planning operation being configured to;

generate and score a first suggested trajectory for an autonomous vehicle, the first suggested trajectory being defined by a first plurality of waypoints;

generate a trajectory gradient based on the first suggested trajectory, the trajectory gradient defining scale and direction data corresponding to each of the first plurality of waypoints of the first suggested trajectory, the scale and direction data defining a second plurality of waypoints;

generate and score a second suggested trajectory defined by the second plurality of waypoints for the autonomous vehicle, the second suggested trajectory being based on the first suggested trajectory, the trajectory gradient, the scale and direction data, and a human driving model; and

output the second suggested trajectory if the score corresponding to the second suggested trajectory is within a score differential threshold relative to the score corresponding to the first suggested trajectory; and

an autonomous vehicle control subsystem in data communication with the data processor, the path planning module, and vehicle control devices, the autonomous vehicle control subsystem configured to receive a trajectory output from the path planning module and to manipulate the vehicle control devices to cause the autonomous vehicle to follow a path conforming to the trajectory received from the path planning module.

2. The system of claim 1 wherein the path planning operation includes machine learnable components.

3. The system of claim 1 wherein the path planning operation is configured to modify parameters of the human driving model based on the trajectory gradient.

4. The system of claim 1 wherein the trajectory gradient is generated by taking the mathematical derivative of a function defining the first suggested trajectory.

5. The system of claim 1 wherein the human driving model is configured to retain information corresponding to human driving behaviors as mathematical or data representations.

6. The system of claim 1 wherein the second suggested trajectory is output to the autonomous vehicle control subsystem causing the autonomous vehicle to follow the second suggested trajectory.

7. The system of claim 1 being further configured to combine the score of the first suggested trajectory with the score of the second suggested trajectory to generate a gradient using a gradient descent process.

8. A method comprising:

generating and scoring, by use of a data processor and a path planning module, a first suggested trajectory for an autonomous vehicle, the first suggested trajectory being defined by a first plurality of waypoints;

generating, by use of the data processor and the path planning module, a trajectory gradient based on the first suggested trajectory, the trajectory gradient defining scale and direction data corresponding to each of the first plurality of waypoints of the first suggested trajectory, the scale and direction data defining a second plurality of waypoints;

generating and scoring, by use of the data processor and the path planning module, a second suggested trajectory defined by the second plurality of waypoints for the autonomous vehicle, the second suggested trajectory being based on the first suggested trajectory, the trajectory gradient, the scale and direction data, and a human driving model;

outputting, by use of the data processor and the path planning module, the second suggested trajectory if the score corresponding to the second suggested trajectory is within a score differential threshold relative to the score corresponding to the first suggested trajectory; and

providing an autonomous vehicle control subsystem in data communication with the data processor, the path planning module, and vehicle control devices, the autonomous vehicle control subsystem receiving a trajectory output from the path planning module and manipulating the vehicle control devices to cause the autonomous vehicle to follow a path conforming to the trajectory received from the path planning module.

9. The method of claim 8 including causing a machine to implement a learnable human driving model.

10. The method of claim 8 including modifying parameters of the human driving model based on the trajectory gradient.

11. The method of claim 8 including generating the trajectory gradient by taking the mathematical derivative of a function defining the first suggested trajectory.

12. The method of claim 8 including causing the human driving model to retain information corresponding to human driving behaviors as mathematical or data representations.

13. The method of claim 8 including outputting the second suggested trajectory to the autonomous vehicle control subsystem causing the autonomous vehicle to follow the second suggested trajectory.

14. The method of claim 8 including combining the score of the first suggested trajectory with the score of the second suggested trajectory to generate a gradient using a gradient descent process.

15. A non-transitory machine-useable storage medium embodying instructions which, when executed by a machine, cause the machine to:

generate and score, by use of a data processor and a path planning module, a first suggested trajectory for an autonomous vehicle, the first suggested trajectory being defined by a first plurality of waypoints;

generate, by use of the data processor and the path planning module, a trajectory gradient based on the first suggested trajectory, the trajectory gradient defining scale and direction data corresponding to each of the first plurality of waypoints of the first suggested trajectory, the scale and direction data defining a second plurality of waypoints;

generate and score, by use of the data processor and the path planning module, a second suggested trajectory defined by the second plurality of waypoints for the autonomous vehicle, the second suggested trajectory being based on the first suggested trajectory, the trajectory gradient, the scale and direction data, and a human driving model;

output, by use of the data processor and the path planning module, the second suggested trajectory if the score corresponding to the second suggested trajectory is within a score differential threshold relative to the score corresponding to the first suggested trajectory; and

cause an autonomous vehicle control subsystem, in data communication with the data processor, the path planning module, and vehicle control devices, to receive a trajectory output from the path planning module and manipulate the vehicle control devices to cause the autonomous vehicle to follow a path conforming to the trajectory received from the path planning module.

16. The non-transitory machine-useable storage medium of claim 15 wherein the instructions are further configured to enable machine learning.

17. The non-transitory machine-useable storage medium of claim 15 wherein the instructions are further configured to modify parameters of the human driving model based on the trajectory gradient.

18. The non-transitory machine-useable storage medium of claim 15 wherein the trajectory gradient is generated by taking the mathematical derivative of a function defining the first suggested trajectory.

19. The non-transitory machine-useable storage medium of claim 15 wherein the human driving model is configured to retain information corresponding to human driving behaviors as mathematical or data representations.

20. The non-transitory machine-useable storage medium of claim 15 wherein the second suggested trajectory is output to the autonomous vehicle control subsystem causing the autonomous vehicle to follow the second suggested trajectory.