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

被引:15
|
作者
Li, Bai [1 ,2 ]
Fan, Lili [3 ]
Ouyang, Yakun [2 ]
Tang, Shiqi [2 ]
Wang, Xiao [4 ]
Cao, Dongpu [5 ]
Wang, Fei-Yue [6 ]
机构
[1] State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China
[2] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China
[3] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[4] Anhui Univ, Sch Artificial Intelligence, Hefei 230039, Peoples R China
[5] Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100084, Peoples R China
[6] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Trajectory; Trajectory planning; Benchmark testing; Planning; Source coding; Location awareness; Automobiles; Automated parking; trajectory planning; motion planning; autonomous driving; autonomous racing; VEHICLES; OPTIMIZATION;
D O I
10.1109/TIV.2022.3228963
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:16 / 21
页数:6
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