Methods and systems for performing lane changes by an autonomous vehicle

Patent metadata

Field	Value
Publication number	US-2021074162-A1
Application number	US-201916564550-A
Country	US
Kind code	A1
Filing date	Sep 9, 2019
Priority date	Sep 9, 2019
Publication date	Mar 11, 2021
Grant date	—

Abstract

Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes: determining, by a processor, that a lane change is desired; determining, by the processor, a lane change action based on a reinforcement learning method and a rule-based method, wherein each of the methods evaluates lane data, vehicle data, map data, and actor data; and controlling, by the processor, the vehicle to perform the lane change based on the lane action.

First claim

What is claimed is:

1 . A method for controlling a vehicle, comprising:
determining, by a processor, that a lane change is desired; determining, by the processor, a lane change action based on a reinforcement learning method and a rule-based method, wherein each of the methods evaluates lane data, map data, vehicle data, and actor data; and controlling, by the processor, the vehicle to perform the lane change based on the lane action.

2 . The method of claim 1 , wherein the rule-based method includes one or more rules that are based on feasibility of control of the vehicle.

3 . The method of claim 1 , wherein the rule-based method includes one or more rules that are based on safety of control of the vehicle.

4 . The method of claim 1 , wherein the rule-based method includes one or more rules that are based on comfort of a user of the vehicle.

5 . The method of claim 1 , wherein the lane change action includes an identifier of a gap between at least two vehicles on the road and a timing for performing the lane change.

6 . The method of claim 1 , wherein the determining the lane change action comprises:
determining the lane change action based on the reinforcement learning method; and determining that the lane change action satisfies constraints of the rule-based method.

7 . The method of claim 6 , further comprising:
determining that the lane change action does not satisfy at least one constraint of the rule-based method; and determining a second lane change action based on the rule-based method, and wherein the lane change action is set to the second lane change action.

8 . The method of claim 7 , further comprising:
determining that the second lane change action does not satisfy at least one rule of the rule-based method; and masking a gap associated with the lane change action from potential gaps; and re-determining the lane change action based on the reinforcement learning method and any remaining potential gaps.

9 . The method of claim 1 , further comprising training the reinforcement learning method based on decisions made by the rule-based method.

10 . A system for controlling a vehicle, comprising:
a non-transitory computer readable medium that stores a reinforcement learning method and a rule-based method that are each based on lane data, map data, vehicle data, and actor data; and a processor configured to: determine that a lane change is desired; determine a lane change action based on the reinforcement learning method and the rule-based method; and control the vehicle to perform the lane change based on the lane action.

11 . The system of claim 10 , wherein the rule-based method includes one or more rules that are based on feasibility of control of the vehicle.

12 . The system of claim 10 , wherein the rule-based method includes one or more rules that are based on safety of control of the vehicle.

13 . The system of claim 10 , wherein the rule-based method includes one or more rules that are based on comfort of a user of the vehicle.

14 . The system of claim 10 , wherein the lane change action includes an identifier of a gap between at least two vehicles on the road and a timing for performing the lane change.

15 . The system of claim 10 , wherein the processor is configured to determine the lane change action by:
determining the lane change action based on the reinforcement learning method; and determining that the lane change action satisfies constraints of the rule-based method.

16 . The system of claim 15 , wherein the processor is further configured to:
determine that the lane change action does not satisfy at least one constraint of the rule-based method; and determine a second lane change action based on the rule-based method, and wherein the lane change action is set to the second lane change action.

17 . The system of claim 16 , wherein the processor is further configured to:
determine that the second lane change action does not satisfy at least one constraint of the rule-based method; and mask a gap associated with the lane change action from potential gaps determined by the reinforcement learning method; and re-determine the lane change action based on the reinforcement learning method and any remaining potential gaps.

18 . The system of claim 10 , wherein the processor is further configured to train the reinforcement learning method based on decisions made by the rule-based method.

19 . The system of claim 18 , wherein the training is performed off-line based on the feedback from the UB agent.

20 . The system of claim 10 , wherein the processor is further configured to translate the lane change action into a trajectory data, and wherein the processor controls the vehicle based on the trajectory data.

Assignees

• Gm Global Tech Operations Llc

Inventors

• Jafari Tafti Sayyed Rouhollah
• Gupta Pinaki
• Mehdi Syed B
• Palanisamy Praveen

Classifications

• B60W30/12

  Lane keeping · CPC title

• B60W40/04

  Traffic conditions · CPC title

• B60W30/18163 Primary

  Lane change; Overtaking manoeuvres · CPC title

• B60W2754/30

  Longitudinal distance · CPC title

• B60W30/095

  Predicting travel path or likelihood of collision · CPC title