Research on Lateral Tracking Control of Low Speed Intelligent Vehicle Using Dynamic Path Preview Model

WANG Lijuan; GUAN Longxin; ZHANG Minghua; SHI Lequan; WANG Aichun; WU Xiaojian

doi:10.13433/j.cnki.1003-8728.20220186

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Research on Lateral Tracking Control of Low Speed Intelligent Vehicle Using Dynamic Path Preview Model

更新时间：2026-07-10

- Research on Lateral Tracking Control of Low Speed Intelligent Vehicle Using Dynamic Path Preview Model
- Mechanical Science and Technology for Aerospace Engineering Vol. 43, Issue 1, Pages: 141-149(2024)
- 作者机构：
  
  1. 南昌大学机电工程学院,南昌,330031
  2. 江铃汽车股份有限公司,南昌,330001
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20220186
  CLC：
- Online First：25 January 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
WANG Lijuan, GUAN Longxin, ZHANG Minghua, et al. Research on Lateral Tracking Control of Low Speed Intelligent Vehicle Using Dynamic Path Preview Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(1): 141-149.
DOI：

WANG Lijuan, GUAN Longxin, ZHANG Minghua, et al. Research on Lateral Tracking Control of Low Speed Intelligent Vehicle Using Dynamic Path Preview Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(1): 141-149. DOI： 10.13433/j.cnki.1003-8728.20220186.

摘要

采用速度自适应的动态预瞄距离是提高智能车侧向跟踪控制效果的有效办法。针对目前基本采用基于当前车速的预瞄距离自适应策略，提出一种结合规划路径和规划速度信息的动态预瞄距离跟踪控制算法以提高智能车在低速侧向跟踪控制的精确性。本文以几何学纯跟踪算法为基础，推导了前行和倒车时的侧向跟踪控制律；依据实时规划的路径和速度信息，设计了预瞄距离动态调整方法，最终获取具有速度自适应性的前视距离-车辆前轮转角关系。最后，在CarSim-MATLAB/Simulink联合仿真环境下验证了算法的有效性和准确性，并通过实车测试，验证了所提出的方法较基于动力学模型的LQR算法具有更低计算消耗和更高跟踪精度。

Abstract

Speed adaptive dynamic preview distance is an effective way to improve the lateral tracking control effect of intelligent vehicle

and as for the problem of using current vehicle speed for the preview distance

a dynamic preview distance tracking control algorithm combining planned path and planned speed information is proposed to improve the accuracy of path tracking at low speed. Based on the pure pursue algorithm

the lateral tracking control laws of moving forward and reversing are derived. According to the real-time planned path and speed information

a dynamic adjustment method of preview distance was designed

and the relationship between the preview distance and the steering angle with speed adaptability was thereby obtained. Finally

the effectiveness and accuracy of the proposed algorithm were verified in CarSim-MATLAB/Simulink co-simulation environment

and a real vehicle test was performed

indicating that the proposed algorithm has lower computational consumption and higher tracking accuracy than the LQR algorithm based on dynamics model.

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