Deep Learning Solutions for Real-Time Driver Distraction and Drowsiness using Alerts

Abstract

The rising incidence of road accidents due to driver distraction highlights the pressing need for robust monitoring systems to improve road safety. The Driver Distraction & Drowsiness Alert System is a significant advancement in this area, utilizing machine learning and deep learning techniques to address the dangers of distracted and drowsy driving. In the past researchers presented drowsy driver detection systems with existing machine learning algorithms. The proposed system focuses on developing a highly accurate detection model capable of identifying subtle signs of driver distraction and drowsiness. The proposed system offers a method for evaluating the level of driver fatigue using various convolutional neural network (CNN) models to analyze images of drivers extracted from video. Driver distraction and facial sleepiness expressions were detected using various features such as eye position, mouth position, head positioning and angle. Beyond the theoretical framework, the system extends its impact through the creation of a practical application. The user-friendly application integrates the sophisticated detection algorithms into real-time driver monitoring, delivering timely alerts to prevent potential accidents caused by fatigue. The system also attempts to send alarming notifications to driver’s relatives in case of an emergency. This paper provides a comprehensive overview of the research methodology, emphasizing the seamless fusion of advanced algorithms and practical application, ultimately contributing to the ongoing efforts aimed at making our roads safer for everyone.