Driver Drowsiness Detection System Using Artificial Intelligence

Abstract

Technology has advanced quickly to enhance the quality of human life. Bicycles were the primary means of transportation during the early 1900s. It's impossible to envision living without a car in the modern day. Although it offers many benefits, there are undoubtedly some life-threatening drawbacks. The chance of an accident increases when a motorist is fatigued, threatening both their own life and the lives of other drivers. Therefore, by identifying the driver's mouth and eye regions and developing a "Driver Drowsiness Detection System," this paper aims to address the serious problem of drowsiness and weariness. Drowsiness is a state between the alertness and tiredness which result in losing focus and leads to accidents. To solve this problem, this system is designed using Artificial Intelligence. The system for detecting exhaustion included yawn detection and EAR (Eye Aspect Ratio), which were later combined with the Convolutional Neural Network model to increase the efficacy of the drowsiness detection model. This paper discussed various AI techniques like Machine Learning, Haar Cascade Classifier, and OpenCV and concludes that this system can detect driver drowsiness using AI and reduce the risk of accidents.