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Learning-Based Lane-Change Behaviour Detection for Intelligent and Connected Vehicles
Luyao Du1, Wei Chen1, Zhonghui Pei2
1School of Automation, Wuhan University of Technology, Wuhan 430070, China.
Computational Intelligence and Neuroscience
|October 16, 2020
Summary
This study introduces a machine learning model for detecting vehicle lane-change behavior on highways using lateral velocity. The developed KNN model achieved high accuracy, enhancing driving safety.
Area of Science:
- Automotive Engineering
- Machine Learning
- Road Safety
Background:
- Lane-change detection is crucial for improving highway driving safety.
- Existing methods may require complex sensor setups or infrastructure.
Purpose of the Study:
- To propose and design a learning-based model for detecting vehicle lane-change behavior in highway environments.
- To identify key features for accurate lane-change detection using machine learning.
Main Methods:
- Utilized the Next Generation Simulation (NGSIM) Interstate 80 Freeway Dataset for analysis.
- Applied machine learning algorithms for feature selection, identifying lateral velocity as a key indicator.
- Developed and trained a K-Nearest Neighbors (KNN) model for lane-change detection.
Main Results:
- Lateral velocity was identified as the most suitable feature for lane-change detection.
- The KNN lane-change detection model demonstrated high performance on selected vehicle data.
- Achieved detection accuracy ranging from 89.57% to 100%.
Conclusions:
- The proposed KNN model effectively detects vehicle lane-change behavior on highways.
- The method offers a reliable approach to enhancing road safety through accurate lane-change detection.
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