A survey of motion planning and control techniques for self-driving urban vehicles

Brian Paden, Michal Čáp, Sze Zheng Yong, Dmitry Yershov, Emilio Frazzoli

Research output: Contribution to journalArticlepeer-review

1625 Scopus citations


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 assistswith system level design choices.

Original languageEnglish (US)
Article number7490340
Pages (from-to)33-55
Number of pages23
JournalIEEE Transactions on Intelligent Vehicles
Issue number1
StatePublished - 2016
Externally publishedYes

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization
  • Artificial Intelligence


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