Towards a system for real-time prevention of drowsiness-related accidents

Abstract

Traffic accidents always result in great human and material losses. One of the main causes of accidents is the human factor, which usually results from driver’s fatigue or drowsiness. To address this issue, several methods for predicting the driver’s state and behavior have been proposed. Some approaches are based on the measurement of the driver’s behavior such as: head movement, blinking time, mouth expression note, while others are based on physiological measurements to obtain information about the internal state of the driver. Several works used machine learning/deep learning to train models for driver behavior prediction. In this paper, we propose a new deep learning architecture based on residual and feature pyramid networks (FPN) for driver drowsiness detection. The trained model is integrated into a system that aims to prevent drowsinessrelated accidents in real-time. The system can detect drivers’ drowsiness in real time and alert the driver in case of danger. Experiment results on benchmarking datasets shows that our proposed architecture achieves high detection accuracy compared to baseline approaches.