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

被引:16
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
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES | 2023年 / 8卷 / 01期
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
相关论文
共 34 条
  • [1] Althoff M, 2017, IEEE INT VEH SYM, P719, DOI 10.1109/IVS.2017.7995802
  • [2] Improved Path Planning by Tightly Combining Lattice-Based Path Planning and Optimal Control
    Bergman, Kristoffer
    Ljungqvist, Oskar
    Axehill, Daniel
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2021, 6 (01): : 57 - 66
  • [3] Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments
    Dolgov, Dmitri
    Thrun, Sebastian
    Montemerlo, Michael
    Diebel, James
    [J]. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2010, 29 (05) : 485 - 501
  • [4] Performance Limit Evaluation by Evolution Test With Application to Automatic Parking System
    Gao, Feng
    Han, Zaidao
    Zhou, Junwu
    Yang, Yiheng
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (04): : 3096 - 3105
  • [5] Trajectory Planning for an Autonomous Vehicle in Spatially Constrained Environments
    Guo, Yuqing
    Yao, Danya
    Li, Bai
    He, Zimin
    Gao, Haichuan
    Li, Li
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (10) : 18326 - 18336
  • [6] Computer vision in automated parking systems: Design, implementation and challenges
    Heimberger, Markus
    Horgan, Jonathan
    Hughes, Ciaran
    McDonald, John
    Yogamani, Senthil
    [J]. IMAGE AND VISION COMPUTING, 2017, 68 : 88 - 101
  • [7] Re-Plannable Automated Parking System With a Standalone Around View Monitor for Narrow Parking Lots
    Jang, Chulhoon
    Kim, Chansoo
    Lee, Seongjin
    Kim, Seokwon
    Lee, Sumyeong
    Sunwoo, Myoungho
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 21 (02) : 777 - 790
  • [8] Falsifying Motion Plans of Autonomous Vehicles With Abstractly Specified Traffic Scenarios
    Klischat, Moritz
    Althoff, Matthias
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (02): : 1717 - 1730
  • [9] Tractor-Trailer Vehicle Trajectory Planning in Narrow Environments With a Progressively Constrained Optimal Control Approach
    Li, Bai
    Acarman, Tankut
    Zhang, Youmin
    Zhang, Liangliang
    Yaman, Cagdas
    Kong, Qi
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2020, 5 (03): : 414 - 425
  • [10] Mixed-Integer and Conditional Trajectory Planning for an Autonomous Mining Truck in Loading/Dumping Scenarios: A Global Optimization Approach
    Li, Bai
    Ouyang, Yakun
    Li, Xiaohui
    Cao, Dongpu
    Zhang, Tantan
    Wang, Yaonan
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (02): : 1512 - 1522