Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

doi:10.1109/TIV.2022.3228963

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

	Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives
	Li, Bai 2,3; Fan, Lili 4; Ouyang, Yakun 2; Tang, Shiqi 2; Wang, Xiao 6; Cao, Dongpu 5; Wang, Fei-Yue 1
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023
卷号	8 期号:1 页码:16-21
关键词	Trajectory Trajectory planning Benchmark testing Planning Source coding Location awareness Automobiles Automated parking trajectory planning motion planning autonomous driving autonomous racing
ISSN号	2379-8858
DOI	10.1109/TIV.2022.3228963
通讯作者	Wang, Xiao(xiao.wang@ahu.edu.cn)
英文摘要	Automated parking is a typical function in a self-driving car. The trajectory planning module directly reflects the intelligence level of an automated parking system. Although many competitions have been launched for autonomous driving, most of them focused on on-road driving scenarios. However, driving on a structured road greatly differs from parking in an unstructured environment. In addition, previous competitions typically competed on the overall driving performance instead of the trajectory planning performance. A trajectory planning competition of automated parking (TPCAP) has been recently organized. This event competed on parking-oriented planners without involving other modules, such as localization, perception, or tracking control. This study reports the TPCAP benchmarks, achievements, experiences, and future perspectives.
资助项目	National Natural Science Foundation of China[62103139] ; Natural Science Foundation of Hunan Province[2021JJ40114] ; 2022 Opening Foundation of State Key Laboratory of Management & Control for Complex Systems[E2S9021119]
WOS关键词	VEHICLES ; OPTIMIZATION
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:000965999800001
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Hunan Province ; 2022 Opening Foundation of State Key Laboratory of Management & Control for Complex Systems
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53239]
专题	多模态人工智能系统全国重点实验室
通讯作者	Wang, Xiao
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China 3.State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China 4.Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China 5.Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100084, Peoples R China 6.Anhui Univ, Sch Artificial Intelligence, Hefei 230039, Peoples R China
推荐引用方式 GB/T 7714	Li, Bai,Fan, Lili,Ouyang, Yakun,et al. Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(1):16-21.
APA	Li, Bai.,Fan, Lili.,Ouyang, Yakun.,Tang, Shiqi.,Wang, Xiao.,...&Wang, Fei-Yue.(2023).Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(1),16-21.
MLA	Li, Bai,et al."Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.1(2023):16-21.