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:由于其增强的便利性、安全优势和潜在的商业价值,智能汽车(IVs)已经引起了全球广泛的关注。尽管一些自动驾驶“独角兽”声称IVs在2025年之前将可商业化部署,但由于各种问题,包括精确计算控制命令或轨迹的规划方法仍是IVs的先决条件,它们的实施仍受到限制,仅限于小规模验证。本文旨在回顾最先进的规划方法,包括管道规划和端对端规划方法。在管道方法方面,提供了一项选择算法的调查,并讨论了扩展和优化机制,而在端对端方法中,驾驶任务的训练方法和验证场景是重点关注的问题。为了帮助读者选择适合的训练和验证方法,这里还对实验平台进行了评估。最后,讨论了当前面临的挑战和未来的方向。本次调查中的并排比较不仅能帮助读者了解所审查的方法的优点和局限性,还能协助系统级的设计选择。