A Comparative Study of Machine Learning Algorithms for Driver Behavior Classification Using Sensor-Based Features

A Comparative Study of Machine Learning Algorithms for Driver Behavior Classification Using Sensor-Based Features

Abstract—Driver behavior classification is a critical component of intelligent transportation systems, enabling proactive road safety interventions and personalized driver assistance. This paper presents a comparative evaluation of five supervised machine learning algorithms — Naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, and Gradient Boosting — applied to the task of classifying three driving behaviors: Normal, Drowsy, and Aggressive. Features are extracted from multi-sensor vehicle data including GPS speed, three-axis accelerometer readings, gyroscope signals, lane deviation measurements, steering entropy, and brake frequency, inspired by the publicly available UAH-DriveSet benchmark. A dataset of 2,100 labeled instances is constructed with deliberate class overlap to simulate real-world ambiguity. Following standard preprocessing and 70/12.5/17.5 train/validation/test split, each model is evaluated on accuracy, precision, recall, F1-score, and five-fold cross-validation accuracy. The Naive Bayes classifier achieves the highest test accuracy of 95.48% and F1-score of 95.47%, demonstrating that carefully engineered sensor features can yield strong classification performance even with lightweight probabilistic models. SVM, Random Forest, and Gradient Boosting each achieve 95.24% accuracy, while KNN trails at 94.52%. Feature importance analysis identifies jerk mean, speed mean, and lane deviation standard deviation as the most discriminative signals. This study confirms that machine learning combined with sensor fusion can effectively support real-time driver monitoring systems.

Keywords-Driver Behavior Classification; Machine Learning; Sensor Fusion; UAH-DriveSet; Random Forest; SVM; Gradient Boosting; Intelligent Transportation Systems