Active collision avoidance method for automated vehicles based on fuzzy PID algorithm

The obstacle avoidance process of automated vehicles is susceptible to interference from issues such as acceleration and minimum collision avoidance distance, which increases the difficulty of automated vehicle active collision avoidance. Therefore, an active collision avoidance method for automated...

Full description

Saved in:
Bibliographic Details
Published in:Automatika Vol. 66; no. 3; pp. 515 - 527
Main Author: Chen, Lu
Format: Journal Article
Language:English
Published: Ljubljana Taylor & Francis Ltd 03.07.2025
Taylor & Francis Group
Subjects:
Acceleration
Algorithms
automated vehicle
Automation
Collision avoidance
Collisions
control sensitivity
Equations of motion
fuzzy adaptive PID
lateral control
Obstacle avoidance
Proportional integral derivative
Quadratic forms
Real time
Vehicles
ISSN:0005-1144, 1848-3380
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The obstacle avoidance process of automated vehicles is susceptible to interference from issues such as acceleration and minimum collision avoidance distance, which increases the difficulty of automated vehicle active collision avoidance. Therefore, an active collision avoidance method for automated vehicles based on a fuzzy Proportional – integral – derivative (PID) algorithm is proposed. Construct a simplified model for automated vehicle active collision avoidance by calculating the motion equation, acceleration, minimum collision avoidance distance, etc. The expected speed is obtained using a linear quadratic form regulator (LQR), and a fuzzy PID controller for automated vehicles is developed by combining the expected speed with the fuzzy PID algorithm to achieve active collision avoidance of automated vehicles. Compared to conventional PID controllers, the fuzzy PID algorithm enhances responsiveness and accuracy across various scenarios. The results of experiments demonstrate that the suggested method improves tracking collision avoidance ability by 25% and collision avoidance stability by 30% in both conventional and emergency states. Additionally, the method ensures safety in emergencies by maintaining real-time adaptability and reliability. The study indicates that the proposed method is 20% more efficient and 15% more reliable than existing active collision avoidance methods, showcasing significant practical application effects.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0005-1144
1848-3380
DOI:10.1080/00051144.2025.2467370