Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey

Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) emerges as a pivotal tool in crafting these BP strategies. This paper offers a comprehensive review of RL-based BP strategies, spotlighting advancements from 2021 to 2023. The authors completely organize and distill the relevant literature, emphasizing paradigm shifts in RL-based BP. Introducing a novel categorization, the authors trace the trajectory of efforts aimed at surmounting practical challenges encountered by autonomous vehicles through innovative RL techniques. To guide readers, the authors furnish a quantitative analysis that maps the volume and diversity of recent RL configurations, elucidating prevailing trends. Additionally, the authors delve into the imminent challenges and potential directions for the future of RL-driven BP in AD. These directions encompass addressing safety vulnerabilities, fostering continual learning capabilities, enhancing data efficiency, championing collaborative vehicular cloud networks, integrating large language models, and enhancing ethical considerations.

• Record URL:
  https://doi.org/10.1016/j.trc.2024.104654
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X2400175X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wu, Jingda
  • 0000-0002-7336-4492
  • Huang, Chao
  • 0000-0003-3023-4388
  • Huang, Hailong
  • Lv, Chen
  • Wang, Yuntong
  • Wang, Fei-Yue
• Publication Date: 2024-7

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References;
• Pagination: 104654
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 164
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Decision making; Driving behavior; Machine learning
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01921547
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 14 2024 10:26AM