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

doi:10.1016/j.trc.2024.104654

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
	Wu, Jingda 4; Huang, Chao 3; Huang, Hailong 4; Lv, Chen 2; Wang, Yuntong 1; Wang, Fei-Yue 1
刊名	TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
	2024-07-01
卷号	164 页码:28
关键词	Autonomous driving Reinforcement learning Behavior planning Decision Autonomous vehicle
ISSN号	0968-090X
DOI	10.1016/j.trc.2024.104654
通讯作者	Huang, Chao(hchao.huang@polyu.edu.hk)
英文摘要	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.
资助项目	CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd.[P0048792]
WOS关键词	DECISION-MAKING ; SAFE ; VEHICLES ; MODEL ; SCENARIOS ; POLICIES ; EFFICIENT ; BARRIER
WOS研究方向	Transportation
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:001244862600001
资助机构	CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58720]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Huang, Chao
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Nanyang 639798, Singapore 3.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China 4.Hong Kong Polytech Univ, Dept Aeronaut & Aviat Engn, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Wu, Jingda,Huang, Chao,Huang, Hailong,et al. Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2024,164:28.
APA	Wu, Jingda,Huang, Chao,Huang, Hailong,Lv, Chen,Wang, Yuntong,&Wang, Fei-Yue.(2024).Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,164,28.
MLA	Wu, Jingda,et al."Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 164(2024):28.