A Real-Time Deep Learning–Based Drowsiness Detection and Alert System for Enhanced Automotive Safety

Abstract

The Comprehensive Drowsiness Detection and Alert Solution for Automotive Safety addresses the growing concern of road accidents caused by driver weariness, which is a factor in about 20% of incidents worldwide. Keeping drivers awake is very important for reducing dangers and saving lives. This research proposes a robust, real-time monitoring system that leverages computer vision and deep learning to detect early signs of fatigue and prevent accidents. The system uses a live camera feed to monitor the driver's face, paying close attention to how their eyes move and blink. A Convolutional Neural Network (CNN) analyses this data to detect long periods of eye closure, a sign of exhaustion. The system sounds an alarm as soon as it sees that the driver is sleepy. This non-intrusive method ensures continuous monitoring without causing discomfort or disruptions. The project is designed to be flexible and scalable so that it can be used as a standalone device in cars or as a mobile app. It makes sure that a wide variety of users can pay and access it by using inexpensive hardware and open-source software. The system can do more than just the basic tasks. It can also be integrated with additional functions, such as heart rate monitoring, vehicle speed control, and yawning recognition, making it much more useful and reliable. This approach can be used not only in personal cars, but also in commercial fleets, public transportation networks, and heavy industrial operations. The bigger effect on society is that it lowers the emotional and financial costs of car accidents, encourages safer driving, and generally makes traffic safer.