ROITP: Road Obstacle-Involved Trajectory Planner for Autonomous Trucks

Yechen Qin, Yiwei Huang, Wenhao Yu, Hong Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Autonomous trucks have the potential to enhance both safety and convenience in intelligent transportation. However, their maximum speed and ability to navigate a variety of driving conditions, particularly uneven roads, are limited by a high center of gravity, which increases the risk of rollover. Road bulges, sinkholes, and unexpected debris all present additional challenges for autonomous trucks' operational design, which current perception and decision-making algorithms often overlook. To mitigate rollover risks and improve adaptability to damaged roads, this paper presents a novel Road Obstacle-Involved Trajectory Planner (ROITP). The planner categorizes road obstacles using a learning-based algorithm. A discrete optimization algorithm selects a multi-objective optimal trajectory while taking into account constraints and objective functions derived from truck dynamics. Validation across various scenarios on a hardware-in-loop platform demonstrates that the proposed planner is effective and feasible for real-time implementation.

Original languageEnglish
Article number9
JournalChinese Journal of Mechanical Engineering (English Edition)
Volume38
Issue number1
DOIs
Publication statusPublished - Dec 2025

Keywords

  • Autonomous truck
  • Obstacle avoiding
  • Polynomial curves
  • Trajectory planning
  • Vehicle stability

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