Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 5: 120 - 120

DOI: https://doi.org/10.1186/s10033-022-00790-5

Advanced Transportation Equipment

Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles

  • Xiaolin Tang ,
  • Kai Yang ,
  • Hong Wang ,
  • Wenhao Yu ,
  • Xin Yang ,
  • Teng Liu ,
  • Jun Li
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  • 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China;
    2. Tsinghua Intelligent Vehicle Design and Safety Research Institute, Tsinghua University, Beijing, 100084, China

收稿日期: 2021-11-02

  修回日期: 2022-07-19

  网络出版日期: 2023-04-24

基金资助

Supported by National Key R&D Program of China (Grant No. 2020YFB1600303), National Natural Science Foundation of China (Grant Nos. U1964203, 52072215) and Chongqing Municipal Natural Science Foundation of China (Grant No. cstc2020jcyj-msxmX0956).

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Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles

  • Xiaolin Tang ,
  • Kai Yang ,
  • Hong Wang ,
  • Wenhao Yu ,
  • Xin Yang ,
  • Teng Liu ,
  • Jun Li
Expand
  • 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China;
    2. Tsinghua Intelligent Vehicle Design and Safety Research Institute, Tsinghua University, Beijing, 100084, China

Received date: 2021-11-02

  Revised date: 2022-07-19

  Online published: 2023-04-24

Supported by

Supported by National Key R&D Program of China (Grant No. 2020YFB1600303), National Natural Science Foundation of China (Grant Nos. U1964203, 52072215) and Chongqing Municipal Natural Science Foundation of China (Grant No. cstc2020jcyj-msxmX0956).

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摘要

Autonomous vehicles require safe motion planning in uncertain environments, which are largely caused by surrounding vehicles. In this paper, a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover. First, a 4-degree of freedom vehicle dynamics model, and a rollover risk index are introduced. Besides, the uncertainty of surrounding vehicles' position is processed and propagated based on the Extended Kalman Filter method. Then, the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles. In addition, the model predictive controller is designed as the motion planning framework which accounts for the rollover risk, the position uncertainty of the surrounding vehicles, and vehicle dynamic constraints of autonomous vehicles. Furthermore, two edge cases, the cut-in scenario, and merging scenario are designed. Finally, the safety, effectiveness, and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.

关键词： Position uncertainty; Rollover prevention; Autonomous vehicles; Motion planning; Model predictive control

本文引用格式

Xiaolin Tang , Kai Yang , Hong Wang , Wenhao Yu , Xin Yang , Teng Liu , Jun Li . Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(5) : 120 -120 . DOI: 10.1186/s10033-022-00790-5

Abstract

Autonomous vehicles require safe motion planning in uncertain environments, which are largely caused by surrounding vehicles. In this paper, a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover. First, a 4-degree of freedom vehicle dynamics model, and a rollover risk index are introduced. Besides, the uncertainty of surrounding vehicles' position is processed and propagated based on the Extended Kalman Filter method. Then, the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles. In addition, the model predictive controller is designed as the motion planning framework which accounts for the rollover risk, the position uncertainty of the surrounding vehicles, and vehicle dynamic constraints of autonomous vehicles. Furthermore, two edge cases, the cut-in scenario, and merging scenario are designed. Finally, the safety, effectiveness, and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.

Key words： Position uncertainty; Rollover prevention; Autonomous vehicles; Motion planning; Model predictive control

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