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Related Experiment Video

Updated: Dec 5, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

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.

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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.