An Adaptive Motion Planning Technique for On-Road Autonomous Driving

Xianjian Jin*, Zeyuan Yan, Guodong Yin, Shaohua Li, Chongfeng Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This paper presents a hierarchical motion planning approach based on discrete optimization method. Well-coupled longitudinal and lateral planning strategies with adaptability features are applied for better performance of on-road autonomous driving with avoidance of both static and moving obstacles. In the path planning level, the proposed method starts with a speed profile designing for the determination of longitudinal horizon, then a set of candidate paths will be constructed with lateral offsets shifting from the base reference. Cost functions considering driving comfort and energy consumption are applied to evaluate each candidate path and the optimal one will be selected as tracking reference afterwards. Re-determination of longitudinal horizon in terms of relative distance between ego vehicle and surrounding obstacles, together with update of speed profile, will be triggered for re-planning if candidate paths ahead fail the safety checking. In the path tracking level, a pure-pursuit-based tracking controller is implemented to obtain the corresponding control sequence and further smooth the trajectory of autonomous vehicle. Simulation results demonstrate the effectiveness of the proposed method and indicate its better performance in extreme traffic scenarios compared to traditional discrete optimization methods, while balancing computational burden at the same time.

Original languageEnglish
Pages (from-to)2655-2664
Number of pages10
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 25 Dec 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation of China under Grant 51905329, in part by the Foundations of State Key Laboratory under Grant KF2020-26, and in part by the National Key Research and Development Program of China under Grant 2016YFB0100906.

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Autonomous driving
  • motion planning
  • obstacle avoidance
  • path generation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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