Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Abstract
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. We completely organize and distill the relevant literature, emphasizing paradigm shifts in RL-based BP. Introducing a novel categorization, we trace the trajectory of efforts aimed at surmounting practical challenges encountered by autonomous vehicles through innovative RL techniques. To guide readers, we furnish a quantitative analysis that maps the volume and diversity of recent RL configurations, elucidating prevailing trends. Additionally, we 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.
- Publication:
-
Transportation Research Part C: Emerging Technologies
- Pub Date:
- July 2024
- DOI:
- 10.1016/j.trc.2024.104654
- Bibcode:
- 2024TRPC..16404654W
- Keywords:
-
- Autonomous driving;
- Reinforcement learning;
- Behavior planning;
- Decision;
- Autonomous vehicle