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.

翻译：智能汽车因其便捷性、安全性优势及潜在商业价值而受到全球关注。尽管预计到2025年实现商业化部署，但目前仍仅限于小规模验证，精确的跟踪控制器和运动规划器是实现智能汽车的关键前提。本文综述了智能汽车领域最先进的运动规划方法，包括流水线规划与端到端规划方法。研究系统分析了流水线方法中的选择、扩展和优化操作，同时探讨了端到端方法中驾驶任务的训练策略与验证场景。本文还回顾了实验平台，以帮助读者选择适当的训练与验证方案。通过并列对比各类方法的优势与局限，为系统级设计提供决策参考。此外，本综述还讨论了当前面临的挑战与未来发展方向。