Development of an AI-Based Driver Drowsiness Detection and Alert System for Accident Prevention
Keywords:
Artificial intelligence, Computer vision, Driver drowsiness detection, Emergency information, Eye aspect ratio, Facial landmarks, Global Positioning System (GPS), PERCLOS, Real-time alertAbstract
Driver drowsiness is a significant safety concern that can impair driving performance and increase the risk of road accidents. This research proposes a Driver Drowsiness Detection System with Location Using Artificial Intelligence as a non-invasive, real-time framework for detecting signs of driver drowsiness through facial and eye movements. The proposed system uses a camera to continuously capture the driver's facial features, while computer vision and facial landmark techniques are employed to localize the face and analyze the eye region. The Eye Aspect Ratio (EAR) is used as a measure of eye openness, and temporal analysis of eye closure is applied to distinguish normal blinking from prolonged eye closure associated with drowsiness. When the extracted features meet the predefined drowsiness criteria for a specified period, the system generates an audible and/or visual alert to notify the driver. In addition, a location and emergency-information module can provide the driver's current location and relevant event information to a designated contact or emergency service. By integrating artificial intelligence, computer vision, real-time monitoring, alert generation, and location-based emergency communication, the proposed system aims to provide a practical and non-intrusive approach to reducing the risks associated with driver fatigue and drowsiness and enhancing overall road safety.
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