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

  • Bai Li
  • , Lili Fan
  • , Yakun Ouyang
  • , Shiqi Tang
  • , Xiao Wang*
  • , Dongpu Cao
  • , Fei Yue Wang
  • *此作品的通讯作者
  • Hunan University
  • School of Artificial Intelligence, Anhui University
  • Tsinghua University
  • CAS - Institute of Automation

科研成果: 期刊稿件文章同行评审

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.

源语言英语
页(从-至)16-21
页数6
期刊IEEE Transactions on Intelligent Vehicles
8
1
DOI
出版状态已出版 - 1 1月 2023

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