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

Authors: Siyu Teng, Xuemin Hu, Peng Deng, Bai Li, Yuchen Li, Yunfeng Ai, Dongsheng Yang, Lingxi Li, Zhe Xuanyuan, Fenghua Zhu, Long Chen

Published: 2023 (Journal Paper)

Source: IEEE Transactions on Intelligent Vehicles

arXiv: 2303.09824

DOI: 10.1109/TIV.2023.3274536

Summary

Surveys both pipeline-based and end-to-end motion planning approaches for autonomous driving, analyzing selection, expansion, and optimization in classical methods alongside deep learning training strategies for end-to-end systems, with experimental comparisons and future challenges.

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 article 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.

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Paper arxiv.org

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arXiv Abstract 2303.09824 arXiv PDF 2303.09824 arXiv HTML 2303.09824 DOI 10.1109/TIV.2023.3274536

Tags

  • Survey

  • Motion planning

  • Autonomous driving

  • End-to-end planning

  • Pipeline planning

  • Deep learning

  • Intelligent vehicles