Driver Drowsiness Detection System Using Machine Learning

Abstract

Drowsy driving is one of the leading causes of traffic accidents all over the world. Driving in a monotonous manner for an extended amount of time without stopping causes tiredness and catastrophic accidents. Drowsiness has the potential to ruin many people's lives. As a result, a real-time system that is simple to create and configure for early and accurate sleepiness detection is required. In this study, a real-time vision-based system called Driver Drowsiness Detection System has been developed utilizing machine learning. In this study, the Haar Cascade classifier was used to recognize the driver's face characteristics and functions present in OpenCV library to detect the region of the face. The following step is to examine the open/close state of the eyes, followed by sluggishness depending on the sequence of ocular conditions. The non-intrusive and cost-effective nature of this vision-based driver tiredness detection is its distinguishing attribute.