Scholarlink

<p>This paper presents an integrated motion planning and control algorithm for high speed autonomous driving at the limits of handling. The proposed framework is developed to enhance vehicle control performance on racetrack driving environments. A hierarchical structure of trajectory planning and motion control is implemented in this paper. First, a model predictive control (MPC) based trajectory planner employs a lightweight, low-fidelity vehicle model to compute optimal path and velocity profiles for the ego vehicle in long spatial horizon. Next, the planned trajectory is tracked by longitudinal acceleration and steering wheel angle commands calculated from the short time horizon longitudinal and lateral motion controllers in a receding horizon manner, ensuring both vehicle stability and precise tracking performance. The effectiveness of the proposed motion planning and control algorithm framework was evaluated through computer simulations. Also, full-size vehicle tests were conducted in actual handling test circuits. The simulation and vehicle test results confirmed that the proposed algorithm is applicable for real world autonomous vehicle systems, effectively enhancing vehicle stability and state tracking performance near the limits of handling.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Vehicle Motion Planning and Control at the Limits of Handling for Autonomous Driving on Racetrack Environments

  • 期刊论文

  • 发表日期：

    2025年6月11日

查看全文
  • Changhee Kim,
  • Myeonggeun Jun,
  • Jayu Kim,
  • Kyongsu Yi,
  • Jaeyong Park

摘要

This paper presents an integrated motion planning and control algorithm for high speed autonomous driving at the limits of handling. The proposed framework is developed to enhance vehicle control performance on racetrack driving environments. A hierarchical structure of trajectory planning and motion control is implemented in this paper. First, a model predictive control (MPC) based trajectory planner employs a lightweight, low-fidelity vehicle model to compute optimal path and velocity profiles for the ego vehicle in long spatial horizon. Next, the planned trajectory is tracked by longitudinal acceleration and steering wheel angle commands calculated from the short time horizon longitudinal and lateral motion controllers in a receding horizon manner, ensuring both vehicle stability and precise tracking performance. The effectiveness of the proposed motion planning and control algorithm framework was evaluated through computer simulations. Also, full-size vehicle tests were conducted in actual handling test circuits. The simulation and vehicle test results confirmed that the proposed algorithm is applicable for real world autonomous vehicle systems, effectively enhancing vehicle stability and state tracking performance near the limits of handling.