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

TL;DR

This survey reviews current motion planning and control algorithms for self-driving urban vehicles, comparing their effectiveness, assumptions, and computational needs to inform system design choices.

Contribution

It provides a comprehensive comparison of state-of-the-art planning and control techniques specifically tailored for urban autonomous driving environments.

Findings

Different approaches vary in vehicle models and environmental assumptions.

The survey highlights strengths and limitations of each technique.

Insights aid in system-level design for urban self-driving vehicles.

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.