Journal
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
Volume 8, Issue 6, Pages 3692-3711Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIV.2023.3274536
Keywords
Motion planning; pipeline planning; end-to-end planning; imitation learning; reinforcement learning; parallel learning
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Intelligent vehicles (IVs) have received global attention for their increased convenience, safety advantages, and potential commercial value. Despite the prediction of commercial deployment by 2025, the implementation of IVs remains limited to small-scale validation, requiring precise tracking controllers and motion planners. This article reviews the state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. It discusses the selection, expansion, and optimization operations in pipeline methods, as well as the 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.
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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