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Computer Science > Robotics

arXiv:2303.09824 (cs)
[Submitted on 17 Mar 2023 (v1), last revised 10 May 2023 (this version, v4)]

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

Authors:Siyu Teng, Xuemin Hu, Peng Deng, Bai Li, Yuchen Li, Dongsheng Yang, Yunfeng Ai, Lingxi Li, Zhe Xuanyuan, Fenghua Zhu, Long Chen
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Abstract: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 paper 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.
Comments: 21 pages, 15 figures and 5 tables, in IEEE Transactions on Intelligent Vehicles
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2303.09824 [cs.RO]
  (or arXiv:2303.09824v4 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2303.09824
Related DOI: https://doi.org/10.1109/TIV.2023.3274536

Submission history

From: Siyu Teng [view email]
[v1] Fri, 17 Mar 2023 08:05:42 UTC (10,080 KB)
[v2] Tue, 21 Mar 2023 12:48:19 UTC (6,429 KB)
[v3] Wed, 29 Mar 2023 10:12:06 UTC (6,429 KB)
[v4] Wed, 10 May 2023 06:50:28 UTC (7,453 KB)
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