A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

TLDR

Self‑driving vehicles are a maturing technology poised to reshape mobility, yet safety‑critical tasks such as motion planning and robust control in dynamic urban environments require diverse approaches that vary in vehicle models, environmental assumptions, and computational demands. The paper surveys state‑of‑the‑art planning and control algorithms for urban self‑driving vehicles. The authors review selected techniques and compare them side‑by‑side, discussing their effectiveness and computational trade‑offs. The comparison provides insight into each approach’s strengths and limitations, aiding system‑level design decisions.

Abstract

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.