Continuous Curvature Turns Based Method for Least Maximum Curvature Path Generation of Autonomous Vehicle

Yu Tian, Zhang Chen, Chuanyi Xue, Yiyong Sun, Bin Liang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Safe driving and stable paths are essential for the autonomous vehicle navigation, that large and fast steering angle should be avoided. This paper addresses a local path generation problem for autonomous vehicles while the least maximum curvature and the shortest length are obtained with limited curvature rate. The properties of the novel feasible paths based on continuous curvature turns are investigated. And a simple and fast computational method is presented to solve the problem by iterative procedure. The simulation in this paper shows that, compared with recent researches, the performance of least maximum curvature and shortest length is obtained by the novel local path planner, and the driving behavior is closer to the human driver operation.

Original languageEnglish
Title of host publicationIECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9781665435543
DOIs
Publication statusPublished - 13 Oct 2021
Externally publishedYes
Event47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 - Toronto, Canada
Duration: 13 Oct 202116 Oct 2021

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2021-October

Conference

Conference47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
Country/TerritoryCanada
CityToronto
Period13/10/2116/10/21

Keywords

  • autonomous vehicle
  • continuous curvature
  • local path planning
  • trajectory generation

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