Intelligent Vehicle Alert System for Drowsy Drivers Using Vision Transformers

Abstract

Road accidents can happen due to various causes but drowsiness, rash driving, drinking, and driving are among the most important factors. Driver drowsiness is a serious threat to road safety. Drowsiness affects the drivers’ sensory, cognitive, and psychomotor abilities, which are necessary for safe driving. Most driver monitoring systems already embedded in vehicles to detect drowsiness use vehicle-based features (i.e., measures) computed by outward-facing cameras for lane tracking or steering wheel angle sensors to analyse lane keeping and steering control behaviour. Though various drowsiness detection systems have been developed during last decade based on many factors, still the systems were demanding an improvement in terms of efficiency, accuracy, cost, speed, and availability, etc. Drowsiness is a term in which a driver feels sleepy while driving and this can detect it by blinking of eyes and yawning. In this project, proposed an integrated approach depends on the eye and mouth closure status (PERCLOS)) face features of the driver. The Face Detector is used to locate the face of a driver in an image and returns as a bounding box or rectangle box values. The Facial Landmark Shape Predictors uses two algorithms namely fixed thresholding and dynamic frame thresholding to extract the facial features that help in calculating essential parameters, i.e., Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR). The two methods use both MOR and EAR parameters to decide the driver’s drowsiness level. Then it Alert or warn the driver by playing sounds or voice message. This helps to find the status of the closed eyes or opened mouth like yawning, and any frame finds that has hand gestures like nodding or covering opened mouth with hand as innate nature of humans when trying to control the sleepiness. This system uses computer vision technology which uses cameras to predict the driver’s fatigue and alert the driver to take a rest.