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

作     者：Wu, Jingda Huang, Chao Huang, Hailong Lv, Chen Wang, Yuntong Wang, Fei-Yue 

作者机构：Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China 

出 版 物：《TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES》 (运输研究C部分：新兴技术)

年 卷 期：2024年第164卷

核心收录：

学科分类：08[工学] 0823[工学-交通运输工程] 

基　　金：CATARC (Tianjin) Automotive Engineering Research Institute Co.  Ltd. [P0048792] 

主　　题：Autonomous driving Reinforcement learning Behavior planning Decision Autonomous vehicle 

摘      要：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.