Thanks to the augmented convenience, safety advantages, and potential commercial value, Intelligent vehicles (IVs) have attracted wide attention throughout the world. Although a few 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：得益于日益增强的便利性、安全性优势及潜在的商业价值，智能车辆（IVs）已引起全球范围内的广泛关注。尽管部分自动驾驶独角兽企业宣称到2025年智能车辆将实现商业化部署，但由于诸多问题的存在，其实际应用仍局限于小规模验证阶段，其中利用规划方法精确计算控制指令或轨迹仍然是智能车辆实现的基础前提。本文旨在综述当前最先进的规划方法，包括管线式规划与端到端规划两类。针对管线式方法，本文系统梳理了算法选择方案，并深入探讨了扩展与优化机制；而在端到端方法中，则重点关注驾驶任务的训练方法和验证场景。为便于读者选择适当的训练与验证方法，本文还对实验平台进行了综述。最后，讨论了当前面临的挑战及未来发展方向。本综述中并行的对比分析不仅能帮助深入理解所评述方法的优势与局限性，还可为系统级设计决策提供支撑。