Rule-Constrained Reinforcement Learning Control for Autonomous Vehicle Left Turn at Unsignalized Intersection

Controlling an autonomous vehicle's unprotected left turn at an intersection is a challenging task. Traditional rule-based autonomous driving decision and control algorithms struggle to construct accurate and trustworthy mathematical models for such circumstances, owing to their considerable uncertainty and unpredictability. To overcome this problem, a rule-constrained reinforcement learning (RCRL) control method is proposed in this work for autonomous driving. To train a reinforcement learning controller with rule constraints, outcomes of the path planning module are used as a goal condition in the reinforcement learning framework. Since they include vehicle dynamics, the proposed approach is safer and more reliable compared to end-to-end learning, thereby ensuring that the generated trajectories are locally optimal while adjusting to unpredictable situations. In the experiments, a highly randomized two-way four-lane intersection is established based on the CARLA simulator to verify the effectiveness of the proposed RCRL control method. Accordingly, the results show that the proposed method can provide real-time safe planning and ensure high passing efficiency for autonomous vehicles in the unprotected left turn task.

• Record URL:
  http://dx.doi.org/10.1049/itr2.12336
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Supplemental Notes:
  • Abstract reprinted with permission of the Institution of Engineering and Technology.
• Authors:
  • Cai, Yingfeng
  • Zhou, Rong
  • Wang, Hai
  • Sun, Xiaoqiang
  • Chen, Long
  • Li, Yicheng
  • Liu, Qingchao
  • He, Youguo
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 2143-2153
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 17
  • Issue Number: 11
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Control systems; Left turns; Machine learning; Unsignalized intersections
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01916380
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 23 2024 10:47AM