Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

doi:10.1109/TIV.2023.3274536

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
	Teng, Siyu 2,3; Hu, Xuemin 4; Deng, Peng 4; Li, Bai 5; Li, Yuchen 2,3,6; Ai, Yunfeng 7; Yang, Dongsheng 8; Li, Lingxi 9; Xuanyuan, Zhe 10; Zhu, Fenghua 11
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-06-01
卷号	8 期号:6 页码:3692-3711
关键词	Motion planning pipeline planning end-to-end planning imitation learning reinforcement learning parallel learning
ISSN号	2379-8858
DOI	10.1109/TIV.2023.3274536
通讯作者	Xuanyuan, Zhe(zhexuanyuan@uic.edu.cn) ; Zhu, Fenghua(fenghua.zhu@ia.ac.cn) ; Chen, Long(long.chen@ia.ac.cn)
英文摘要	Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This article reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.
资助项目	NSFC[62273135] ; NSFC[2021CFB460] ; Natural Science Foundation of Hubei Province in China[62103139] ; 2022 Opening Foundation of State Key Laboratory of Management and Control for Complex Systems[E2S9021119] ; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College[2022B1212010006] ; Guangdong Higher Education Upgrading Plan with UIC Research[R0400001-22] ; Guangdong Higher Education Upgrading Plan with UIC Research[R201902]
WOS关键词	PARALLEL INTELLIGENCE ; OPTIMIZATION ; METAVERSES ; SCENARIOS ; VEHICLES ; VISION ; FRAMEWORK ; MODEL ; CAR
WOS研究方向	Computer Science ; Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001033547600017
资助机构	NSFC ; Natural Science Foundation of Hubei Province in China ; 2022 Opening Foundation of State Key Laboratory of Management and Control for Complex Systems ; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College ; Guangdong Higher Education Upgrading Plan with UIC Research
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53924]
专题	多模态人工智能系统全国重点实验室
通讯作者	Xuanyuan, Zhe; Zhu, Fenghua; Chen, Long
作者单位	1.Waytous Ltd, Qingdao, Peoples R China 2.BNU HKBU United Int Coll, Zhuhai 519087, Peoples R China 3.Hong Kong Baptist Univ, Kowloon, Hong Kong 999077, Peoples R China 4.Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China 5.State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China 6.Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 8.Jinan Univ, Sch Publ Management Emergency Management, Guangzhou 510632, Peoples R China 9.Indiana Univ Purdue Univ, Purdue Sch Engn & Technol, Indianapolis, IN USA 10.BNU HKBU United Int Coll, Guangdong Prov Key Lab IRADS, Zhuhai, Peoples R China
推荐引用方式 GB/T 7714	Teng, Siyu,Hu, Xuemin,Deng, Peng,et al. Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(6):3692-3711.
APA	Teng, Siyu.,Hu, Xuemin.,Deng, Peng.,Li, Bai.,Li, Yuchen.,...&Chen, Long.(2023).Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(6),3692-3711.
MLA	Teng, Siyu,et al."Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.6(2023):3692-3711.