  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.
