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

TLDR

Intelligent vehicles promise greater convenience, safety, and commercial value, yet despite 2025 deployment forecasts, real‑world implementation remains limited, underscoring the need for precise tracking controllers and motion planners. This review surveys state‑of‑the‑art motion‑planning approaches for intelligent vehicles, covering both pipeline and end‑to‑end methods. It analyzes pipeline operations—selection, expansion, optimization—and examines training and validation strategies for end‑to‑end methods, while reviewing experimental platforms to guide method selection. A comparative assessment of the methods reveals their respective strengths and limitations, and the survey identifies current challenges and outlines future research directions.

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.