System and method for autonomous vehicle control to minimize energy cost

• US 10,678,234 B2
• Filed: 08/24/2017
• Issued: 06/09/2020
• Est. Priority Date: 08/24/2017
• Status: Active Grant

• Current Assignee: TuSimple, Inc. (TuSimple Holdings, Inc.)
• Original Assignee: TuSimple, Inc. (TuSimple Holdings, Inc.)
• Inventors: Sun, Xing, Lin, Wutu, Liu, Liu, Ma, Kai-Chieh, Xuan, Zijie, Zhao, Yufei
• Primary Examiner(s): Kan, Yuri
• Application Number:
  US15/685,715
• Publication Number:
  US 20190064793A1
• Time in Patent Office: 1,020 Days
• CPC Class Codes: B60R 16/0236 for economical driving
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G05D 1/0005 with arrangements to save energy
• 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 1/228 Command input arrangements located on-board unmanned vehicles
• CPC Class Codes: G05D 1/644 Optimisation of travel parameters, e.g. of energy consumption, journey time or distance
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration

Abstract

A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

Claims

1. A system comprising:

a data processor; and

an energy-optimized motion planning module, executable by the data processor, the energy-optimized motion planning module being configured to perform an energy-optimized motion planning operation for autonomous vehicles, the energy-optimized motion planning operation being configured to;

receive sensor data from a plurality of sensors on an autonomous vehicle;

generate a plurality of potential routings and related vehicle motion control operations for the autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination, the related vehicle motion control operations including vehicle motion control operations to adjust the autonomous vehicle'"'"'s speed, acceleration, steering direction, braking level, and to avoid obstacles detected in the proximity of the autonomous vehicle;

generate an energy consumption rate for each of the potential routings and related vehicle motion control operations using a trainable vehicle energy consumption model and the sensor data, the energy consumption rate for each of the potential routings and related vehicle motion control operations being generated from the sensor data and machine learning datasets configured from test data collections produced from prior real-world training scenarios;

score each of the plurality of potential routings and related vehicle motion control operations based on the corresponding energy consumption rate;

select one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range;

modify the related vehicle motion control operations to lower the autonomous vehicle'"'"'s energy consumption over a corresponding potential routing if the score of the corresponding potential routing is not within the acceptable range; and

output a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

2. The system of claim 1 wherein the sensor data includes a type of data from the group consisting of:

leading vehicle drafting characteristics, road inclination, road surface friction, wind speed, atmospheric pressure, and external temperature.

3. The system of claim 1 being further configured to receive environmental map data.

4. The system of claim 1 wherein the related vehicle motion control operations include vehicle motion control operations to adjust the autonomous vehicle'"'"'s motor torque and gear usage.

5. The system of claim 1 wherein the related vehicle motion control operations include vehicle motion control operations to cause the vehicle to draft a leading vehicle.

6. The system of claim 1 wherein the vehicle motion control output is provided to a vehicle control subsystem causing the autonomous vehicle to follow a routing and perform vehicle motion control operations corresponding to the vehicle motion control output.

18. The system of claim 1 being further configured to generate a predicted energy consumption rate for a potential routing based on predicted vehicle motion control operations for a particular vehicle using the trainable vehicle energy consumption model.

7. A method comprising:

receiving sensor data from a plurality of sensors on an autonomous vehicle;

generating a plurality of potential routings and related vehicle motion control operations for the autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination, the related vehicle motion control operations including vehicle motion control operations to adjust the autonomous vehicle'"'"'s speed, acceleration, steering direction, braking level, and to avoid obstacles detected in the proximity of the autonomous vehicle;

generating an energy consumption rate for each of the potential routings and related vehicle motion control operations using a trainable vehicle energy consumption model and the sensor data, the energy consumption rate for each of the potential routings and related vehicle motion control operations being generated from the sensor data and machine learning datasets configured from test data collections produced from prior real-world training scenarios;

scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding energy consumption rate;

selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range;

modifying the related vehicle motion control operations to lower the autonomous vehicle'"'"'s energy consumption over a corresponding potential routing if the score of the corresponding potential routing is not within the acceptable range; and

outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

8. The method of claim 7 wherein the sensor data includes a type of data from the group consisting of:

leading vehicle drafting characteristics, road inclination, road surface friction, wind speed, atmospheric pressure, and external temperature.

9. The method of claim 7 including positioning the autonomous vehicle relative to other vehicles.

10. The method of claim 7 wherein the related vehicle motion control operations include vehicle motion control operations to adjust the autonomous vehicle'"'"'s motor torque and gear usage.

11. The method of claim 7 wherein the related vehicle motion control operations include vehicle motion control operations to cause the vehicle to draft a leading vehicle.

12. The method of claim 7 wherein the vehicle motion control output is provided to a vehicle control subsystem causing the autonomous vehicle to follow a routing and perform vehicle motion control operations corresponding to the vehicle motion control output.

19. The method of claim 7 including generating a predicted energy consumption rate for a potential routing based on predicted vehicle motion control operations for a particular vehicle using the trainable vehicle energy consumption model.

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

receive sensor data from a plurality of sensors on an autonomous vehicle;

generate a plurality of potential routings and related vehicle motion control operations for the autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination, the related vehicle motion control operations including vehicle motion control operations to adjust the autonomous vehicle'"'"'s speed, acceleration, steering direction, braking level, and to avoid obstacles detected in the proximity of the autonomous vehicle;

generate an energy consumption rate for each of the potential routings and related vehicle motion control operations using a trainable vehicle energy consumption model and the sensor data, the energy consumption rate for each of the potential routings and related vehicle motion control operations being generated from the sensor data and machine learning datasets configured from test data collections produced from prior real-world training scenarios;

score each of the plurality of potential routings and related vehicle motion control operations based on the corresponding energy consumption rate;

select one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range;

modify the related vehicle motion control operations to lower the autonomous vehicle'"'"'s enemy consumption over a corresponding potential routing if the score of the corresponding potential routine is not within the acceptable ramie; and

output a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

14. The non-transitory machine-useable storage medium of claim 13 wherein the sensor data includes a type of data from the group consisting of:

leading vehicle drafting characteristics, road inclination, road surface friction, wind speed, atmospheric pressure, and external temperature.

15. The non-transitory machine-useable storage medium of claim 13 being further configured to position the autonomous vehicle relative to other vehicles.

16. The non-transitory machine-useable storage medium of claim 13 wherein the related vehicle motion control operations include vehicle motion control operations to cause the vehicle to draft a leading vehicle.

17. The non-transitory machine-useable storage medium of claim 13 wherein the vehicle motion control output is provided to a vehicle control subsystem causing the autonomous vehicle to follow a routing and perform vehicle motion control operations corresponding to the vehicle motion control output.