Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral Method

Intelligent driving technology can effectively improve transportation efficiency and vehicle safety and has become a development trend in automotive technology. As one of the core technologies of autonomous driving, path tracking control is directly related to the driving safety and comfort of vehic...

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Main Authors: Yingjie Liu, Qianqian Wang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:World Electric Vehicle Journal
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Online Access:https://www.mdpi.com/2032-6653/16/5/262
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author Yingjie Liu
Qianqian Wang
author_facet Yingjie Liu
Qianqian Wang
author_sort Yingjie Liu
collection DOAJ
description Intelligent driving technology can effectively improve transportation efficiency and vehicle safety and has become a development trend in automotive technology. As one of the core technologies of autonomous driving, path tracking control is directly related to the driving safety and comfort of vehicles and therefore has become a key research area of autonomous driving technology. In order to improve the reliability and control accuracy of path tracking algorithms, this paper proposed a path tracking control method based on the Gaussian pseudospectral method. Firstly, a vehicle motion model was constructed, and then an optimal trajectory solving method based on the hp adaptive pseudospectral method was proposed. The optimal trajectory control problem with differential constraints was transformed into an algebraic constrained nonlinear programming problem and solved using the sequential quadratic programming and compared with traditional control methods. The simulation results show that the tracking error of the lateral distance under the condition of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>μ</mi><mo>=</mo><mn>0.8</mn></mrow></semantics></math></inline-formula> is smaller than that of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>μ</mi><mo>=</mo><mn>0.4</mn></mrow></semantics></math></inline-formula>. At the same time, the tracking error of the lateral distance under the condition of <i>u</i> = 30 km/h is smaller than that of <i>u</i> = 90 km/h. The optimal path tracking control using the improved hp adaptive pseudospectral method has higher accuracy and better control effect compared to traditional control algorithms. Finally, virtual and real vehicle tests were conducted to verify the effectiveness and accuracy of the improved hp adaptive trajectory control algorithm.
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spelling doaj-art-597ecff73f3141829369b3bd646c10b22025-08-20T01:56:45ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-05-0116526210.3390/wevj16050262Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral MethodYingjie Liu0Qianqian Wang1School of Machinery and Automation, Weifang University, Weifang 261061, ChinaSchool of Machinery and Automation, Weifang University, Weifang 261061, ChinaIntelligent driving technology can effectively improve transportation efficiency and vehicle safety and has become a development trend in automotive technology. As one of the core technologies of autonomous driving, path tracking control is directly related to the driving safety and comfort of vehicles and therefore has become a key research area of autonomous driving technology. In order to improve the reliability and control accuracy of path tracking algorithms, this paper proposed a path tracking control method based on the Gaussian pseudospectral method. Firstly, a vehicle motion model was constructed, and then an optimal trajectory solving method based on the hp adaptive pseudospectral method was proposed. The optimal trajectory control problem with differential constraints was transformed into an algebraic constrained nonlinear programming problem and solved using the sequential quadratic programming and compared with traditional control methods. The simulation results show that the tracking error of the lateral distance under the condition of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>μ</mi><mo>=</mo><mn>0.8</mn></mrow></semantics></math></inline-formula> is smaller than that of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>μ</mi><mo>=</mo><mn>0.4</mn></mrow></semantics></math></inline-formula>. At the same time, the tracking error of the lateral distance under the condition of <i>u</i> = 30 km/h is smaller than that of <i>u</i> = 90 km/h. The optimal path tracking control using the improved hp adaptive pseudospectral method has higher accuracy and better control effect compared to traditional control algorithms. Finally, virtual and real vehicle tests were conducted to verify the effectiveness and accuracy of the improved hp adaptive trajectory control algorithm.https://www.mdpi.com/2032-6653/16/5/262vehicle dynamicstrajectory trackingoptimal controlhp adaptive pseudospectral method
spellingShingle Yingjie Liu
Qianqian Wang
Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral Method
World Electric Vehicle Journal
vehicle dynamics
trajectory tracking
optimal control
hp adaptive pseudospectral method
title Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral Method
title_full Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral Method
title_fullStr Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral Method
title_full_unstemmed Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral Method
title_short Trajectory Tracking in Autonomous Driving Based on Improved hp Adaptive Pseudospectral Method
title_sort trajectory tracking in autonomous driving based on improved hp adaptive pseudospectral method
topic vehicle dynamics
trajectory tracking
optimal control
hp adaptive pseudospectral method
url https://www.mdpi.com/2032-6653/16/5/262
work_keys_str_mv AT yingjieliu trajectorytrackinginautonomousdrivingbasedonimprovedhpadaptivepseudospectralmethod
AT qianqianwang trajectorytrackinginautonomousdrivingbasedonimprovedhpadaptivepseudospectralmethod