Enhanced Driver Drowsiness Detection using Deep Learning

Abstract

The primary reason for road accidents is drowsiness reported by National Highway Traffic Safety Administration (NHTSA). To overcome this issue, researchers have proposed and implemented various methods based on driver behaviour and vehicle movements. Vehicle-based methods often rely on a set of predetermined parameters to detect drowsiness, such as changes in steering wheel angle or lane deviation. However, these parameters may not always accurately reflect a driver’s level of alertness. Therefore, it is essential to develop an effective approach for driver drowsiness detection. Deep learning techniques such as convolutional neural networks (CNN) are structured solutions to detect drowsiness based on drivers’ facial features. The proposed approach based on CNN focuses on the eyes and mouth region using the nose as a central point. CNN is operated with rectified linear activation function (ReLU) which gives 94.95% accuracy as compared to existing methods even in different situations namely low light, different angles, and transparent glasses.