Thanks to the augmented convenience, safety advantages, and potential commercial value, Intelligent vehicles (IVs) have attracted wide attention throughout the world. Although a few of autonomous driving unicorns assert that IVs will be commercially deployable by 2025, their implementation is still restricted to small-scale validation due to various issues, among which precise computation of control commands or trajectories by planning methods remains a prerequisite for IVs. This paper aims to review state-of-the-art planning methods, including pipeline planning and end-to-end planning methods. In terms of pipeline methods, a survey of selecting algorithms is provided along with a discussion of the expansion and optimization mechanisms, whereas in end-to-end methods, the training approaches and verification scenarios of driving tasks are points of concern. Experimental platforms are reviewed to facilitate readers in selecting suitable training and validation methods. Finally, the current challenges and future directions are discussed. The side-by-side comparison presented in this survey not only helps to gain insights into the strengths and limitations of the reviewed methods but also assists with system-level design choices.

翻译：得益于其增强的便利性、安全优势及潜在商业价值，智能车辆（IV）在世界范围内获得了广泛关注。尽管部分自动驾驶独角兽企业宣称IV将在2025年前实现商业化部署，但其实际应用仍局限于小规模验证，这主要归因于诸多问题，其中通过规划方法精确计算控制指令或轨迹仍是IV应用的前提条件。本文旨在综述先进规划方法，包括流水线式规划与端到端式规划方法。针对流水线方法，本文梳理了选用算法，并探讨了扩展与优化机制；而在端到端方法中，驾驶任务的训练方法与验证场景则成为关注重点。本文还综述了实验平台，以帮助读者选择适配的训练与验证方法。最后，本文讨论了当前面临的挑战与未来发展方向。本综述通过并列对比分析，不仅有助于深入理解所评述方法的优势与局限，还可为系统级设计决策提供支持。