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.

该论文旨在综述智能车辆的规划方法，介绍了管道规划和端到端规划方法，并探讨了挑选算法和训练方法的不同考虑因素以及当前的挑战和未来方向。