DRIVER DROWSINESS DETECTION USING AI TECHNIQUES

Abstract

The increasing emphasis on road safety has led to a 
heightened focus on detecting and preventing driver 
drowsiness, a major cause of accidents worldwide. To 
address this issue, researchers and engineers have 
turned to machine learning and computer vision 
techniques, particularly Convolutional Neural 
Networks (CNNs), to develop robust driver 
drowsiness detection systems. CNNs analyze real-time 
visual cues from the driver's face and surroundings, 
enabling accurate detection of drowsiness signs and 
triggering timely alerts or safety measures to prevent 
accidents. This paper aims to explore the significance 
of driver drowsiness detection, the challenges 
involved, and the potential of CNNs in overcoming 
these challenges. It discusses the architecture of CNN
based drowsiness detection systems, dataset 
requirements, training methodologies, and evaluation 
metrics.