Driver Behavior Classification System Analysis Using Machine Learning Methods

Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on road, therefore increasing risk of accidents. As consequence, number accidents increases infrastructure damaged. Cars are now equipped with different safety precautions that ensure awareness at all times. The first step for such systems to define whether distracted or not. Different methods proposed detect distractions, but they lack efficiency tested real-life situations. In this paper, four machine learning classification implemented compared identify drivers’ behavior distraction situations based real data corresponding behaviors as aggressive, drowsy normal. were randomized better application methods. We demonstrate gradient boosting method outperforms other used classifiers.