Intelligent vehicles (IVs) have attracted wide attention thanks to the augmented convenience, safety advantages, and potential commercial value. Although a few of autonomous driving unicorns assert that IVs will be commercially deployable by 2025, their deployment is still restricted to small-scale validation due to various issues, among which safety, reliability, and generalization of planning methods are prominent concerns. Precise computation of control commands or trajectories by planning methods remains a prerequisite for IVs, owing to perceptual imperfections under complex environments, which pose an obstacle to the successful commercialization of 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 helps to gain insights into the strengths and limitations of the reviewed methods, which also assists with system-level design choices.

翻译：智能车辆凭借其提升的便利性、安全优势及潜在商业价值而受到广泛关注。尽管部分自动驾驶独角兽企业宣称智能车辆将在2025年前实现商业部署，但由于诸多问题——其中规划方法的安全性、可靠性和泛化能力尤为突出——其部署仍局限于小规模验证。在复杂环境下感知存在缺陷的情况下，精确计算控制指令或轨迹仍是智能车辆成功商业化的前提。本文旨在综述现有最先进的规划方法，包括管线式规划与端到端规划方法。在管线式方法方面，本文综述了算法选择方法，并探讨了扩展与优化机制；而在端到端方法方面，训练方法与驾驶任务验证场景是关注重点。本文还回顾了实验平台，以帮助读者选择合适的训练与验证方法。最后，讨论了当前面临的挑战与未来发展方向。本综述中进行的并列比较有助于深入理解所评述方法的优势与局限性，同时为系统级设计选择提供支持。