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Abstract

To address the issues of low adaptability and significant tracking errors in parking scenarios when using fixed look-ahead distance Pure Pursuit (PP) algorithms, this paper proposes an automatic parking path tracking control algorithm based on Fuzzy Pure Pursuit (FPP). Considering the influence of road curvature on look-ahead distance, a fuzzy controller is designed to output speed proportionality coefficient and curvature proportionality coefficient. This enables adaptive adjustment of the look-ahead distance according to vehicle speed and road curvature, thereby enhancing path adaptability and tracking accuracy. Prescan/CarSim/Simulink simulation results demonstrate that in vertical parking scenarios, the FPP-based tracking control algorithm outperforms traditional PP algorithms in tracking performance for desired paths and heading angles. The tracking error is reduced by 4.8%, and the heading angle error is reduced by 7.3%. The test results of the Apollo advanced platform show that, under different initial heading angles, the vehicle is able to successfully track the parking path and completes the parking operation without collisions. The tracking control algorithm based on FPP has excellent environmental adaptability.

Details

1009240
Title
Virtual parking path planning in narrow roads based on fuzzy pure pursuit algorithm
Publication title
PLoS One; San Francisco
Volume
20
Issue
12
First page
e0335911
Number of pages
16
Publication year
2025
Publication date
Dec 2025
Section
Research Article
Publisher
Public Library of Science
Place of publication
San Francisco
Country of publication
United States
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Milestone dates
2025-07-21 (Received); 2025-10-19 (Accepted); 2025-12-29 (Published)
ProQuest document ID
3288245427
Document URL
https://www.proquest.com/scholarly-journals/virtual-parking-path-planning-narrow-roads-based/docview/3288245427/se-2?accountid=208611
Copyright
© 2025 Men et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-12-30
Database
ProQuest One Academic