Vehicle control and trip planning system

Patent metadata

Field	Value
Publication number	US-11720113-B2
Application number	US-201916719165-A
Country	US
Kind code	B2
Filing date	Dec 18, 2019
Priority date	Dec 18, 2019
Publication date	Aug 8, 2023
Grant date	Aug 8, 2023

Abstract

A vehicle control system and method includes identifying a segment of a route where one or more first vehicle systems were operated manually instead of operated by one or more processors according to one or more trip plans during prior traversals of the segment by the one or more first vehicle systems. A segment plan is generated for traversing the segment under control of the one or more processors. The segment plan is generated based on how the one or more first vehicle systems were manually operated during the prior traversals of the segment. One or more second vehicle systems are controlled with the one or more processors to traverse the segment according to the segment plan.

First claim

What is claimed is:

1. A method comprising:
identifying a segment of a route where one or more first vehicle systems were operated manually instead of operated by one or more processors according to one or more trip plans during prior traversals of the segment by the one or more first vehicle systems;
generating a machine-learned trip plan for traversing the segment under control of the one or more processors, the machine-learned trip plan being generated based on how the one or more first vehicle systems were manually operated during the prior traversals of the segment; and
controlling one or more second vehicle systems with the one or more processors to traverse the segment according to the machine-learned trip plan.

2. The method of claim 1 , further comprising determining that the prior traversals of the segment by the one or more first vehicle systems caused handling of the one or more first vehicle systems to violate a designated limit.

3. The method of claim 2 , further comprising controlling the one or more second vehicle systems according to the machine-learned trip plan to prevent the one or more second vehicle systems from violating the designated limit.

4. The method of claim 2 , wherein the designated limit is one or more handling parameters of the one or more first vehicle systems.

5. The method of claim 1 , further comprising replacing a portion of the one or more trip plans where the one or more first vehicle systems were operated manually instead of operated by the one or more processors with the machine-learned trip plan.

6. The method of claim 1 , further comprising receiving the machine-learned trip plan from an off-board database.

7. The method of claim 1 , wherein the one or more trip plans include one or more trip plan operating parameters, wherein the machine-learned trip plan includes one or more segment operating parameters of the one or more second vehicle systems that are different than the one or more trip plan operating parameters.

8. The method of claim 1 , further comprising controlling the one or more second vehicle systems according to the machine-learned trip plan while the one or more second vehicle systems are moving within the segment of the route.

9. The method of claim 1 , wherein the segment of the route is a first segment of the route, further comprising identifying a second segment of the route where the one or more first vehicle systems were operated by the one or more processors according to the one or more trip plans during prior traversals of the second segment of the route, and controlling the one or more second vehicle systems according to the trip plan while the one or more second vehicle systems move within the second segment of the route.

10. The method of claim 1 , wherein the segment of the route is a first segment of the route where the one or more first vehicle systems were operated manually instead of operated by the one or more processors according to the one or more trip plans, further comprising identifying a second segment of the route where the one or more first vehicle systems were operated manually instead of operated by the one or more processors according to the one or more trip plans during prior traversals of the second segment by the one or more first vehicle systems, and generating a second machine-learned trip plan for traversing the second segment under control by the one or more processors.

11. The method of claim 10 , further comprising determining that prior movement of the one or more first vehicle systems through the first segment of the route caused handling of the one or more first vehicle systems to violate a first designated limit, and determining that prior movement of the one or more first vehicle systems through the second segment of the route caused handling of the one or more first vehicle systems to violate a second designated limit.

12. The method of claim 1 , further comprising determining a difference between one or more operating settings of the one or more first vehicle systems operated manually instead of operated by the one or more processors and one or more operating settings of the one or more second vehicle systems according to the machine-learned trip plan.

13. A vehicle control system comprising:
a controller configured to identify a segment of a route where one or more first vehicle systems were operated manually instead of operated by one or more processors according to one or more trip plans during prior traversals of the segment by the one or more first vehicle systems,
wherein the controller is configured to generate a machine-learned trip plan for traversing the segment under control of the one or more processors, the machine-learned trip plan being generated based on how the one or more first vehicle systems were manually operated during the prior traversals of the segment, and
wherein the controller is configured to control one or more second vehicle systems to traverse the segment according to the machine-learned trip plan.

14. The vehicle control system of claim 13 , wherein the controller is configured to determine that the prior traversals of the segment by the one or more first vehicle systems caused handling of the one or more first vehicle systems to violate a designated limit.

15. The vehicle control system of claim 14 , wherein the controller is configured to control the one or more second vehicle systems according to the machine-learned trip plan to prevent the one or more second vehicle systems from violating the designated limit.

16. The vehicle control system of claim 14 , wherein the designated limit is one or more handling parameters of the one or more first vehicle systems.

17. The vehicle control system of claim 13 , wherein the controller is configured to replace a portion of the one or more trip plans where the one or more first vehicle systems were operated manually instead of operated by the one or more processors with the machine-learned trip plan.

18. The vehicle control system of claim 13 , wherein the controller is configured to receive the machine-learned trip plan from an off-board database.

19. The vehicle control system of claim 13 , wherein the one or more trip plans include one or more trip plan operating parameters, wherein the machine-learned trip plan includes one or more segment operating parameters of the one or more second vehicle systems that are different than the one or more trip plan operating parameters.

20. A method comprising:
identifying a segment of a route where one or more first vehicle systems were operated manually instead of operated by one or more processors onboard the one or more first vehicle systems according to one or more trip plans during prior traversals of the segment by the one or more first vehicle systems;
generating a machine-learned trip plan for traversing the segment under control of the one or more processors, the machine-learned trip plan being generated based on how the one or more first vehicle systems were manually operated during the prior traversals of the segment;
replacing a portion of the one or more trip plans where the one or more first vehicle systems were operated manually instead of operated by the one or more processors with the machine-learned trip plan; and
controlling one or more second vehicle systems with one or more processors onboard the one or more second vehicle systems to traverse the segment according to the machine-learned trip plan.

Assignees

• Westinghouse Air Brake Tech Corp

Inventors

• Ghosh Dipankar
• Corry James Glen
• Schoonmaker William Cherrick
• Kraeling Mark Bradshaw

Classifications

• B61L27/04

  Automatic systems, e.g. controlled by train; Change-over to manual control · CPC title

• G05D1/0221 Primary

  involving a learning process · CPC title

• B61L15/0062

  On-board target speed calculation or supervision · CPC title

• G05D1/0274

  using mapping information stored in a memory device (navigation using map-matching G01C21/30) · CPC title

• G05D1/0061

  for transition from automatic pilot to manual pilot and vice versa · CPC title