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Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning

Anik Das¹, Md Nasim Khan¹, Mohamed M Ahmed¹

¹University of Wyoming, Department of Civil & Architectural Engineering, 1000 E University Ave, Dept. 3295, Laramie, WY, 82071, United States.

Accident; Analysis and Prevention

|May 15, 2020

Summary

This summary is machine-generated.

This study developed advanced lane change detection models using diverse data, achieving 95.9% accuracy. The system can predict lane changes up to 5 seconds in advance, enhancing driving safety.

Keywords:

Artificial neural network Connected vehicle Lane change detection Naturalistic driving study Random Forest Support vector machine eXtrem gradient boosting

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Area of Science:

Transportation Engineering
Artificial Intelligence
Traffic Safety

Background:

Lane changing is a complex driving maneuver critical for traffic safety research.
Real-time lane change detection systems are needed to assist drivers in complex scenarios.
Existing systems may lack robustness across various conditions.

Purpose of the Study:

To propose and evaluate trajectory-level lane change detection models.
To investigate the influence of vehicle kinematics, machine vision, roadway characteristics, and driver demographics on lane change detection.
To assess model performance under different weather conditions and data availability scenarios.

Main Methods:

Utilized the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID) datasets.
Employed Boruta algorithm for feature selection across six data fusion categories.
Trained and validated models using Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtreme Gradient Boosting (XGBoost).

Main Results:

Achieved a highest overall detection accuracy of 95.9% using XGBoost with all features.
Demonstrated 81.9% accuracy using RF with only vehicle kinematics, showing utility with limited data.
Early detection capability predicted lane changes up to 5 seconds before lane crossing.

Conclusions:

The developed models effectively detect lane changes using multi-source data, including weather conditions.
The system offers robust performance even with limited data (vehicle kinematics only).
The models can enhance driver monitoring and control in Cooperative Automated Vehicle environments.