Path Planning for Autonomous Driving: The State of the Art and Perspectives

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.