Authors: Sk.Firdaus Fathima, M.Anusha, S.Vennela, Sk.Sadiya Kousar, B.Gayathri
Abstract: Driver drowsiness is one of the major causes of road accidents globally, leading to serious injuries, deaths, and economic loss. To combat this, a real-time Driver Drowsiness Detection System has been implemented using machine learning algorithms combined with IoT hardware. The system tracks the driver's eyes continuously through a real-time video feed obtained via a webcam. With the OpenCV and dlib libraries, the Eye Aspect Ratio (EAR) is computed to obtain a measurement of the degree of eye closure, which is a good predictor of drowsiness. Upon detection of prolonged eye closure, the system sends a serial communication command to an Arduino Uno microcontroller to activate a buzzer alarm and commence a progressive motor deceleration, mimicking a safe vehicle stop. This two-stage mechanism reduces the risk of accidents by giving both an initial warning and an automatic safety measure. Experimental data show that the designed system has a detection accuracy of 96.8% for different illumination conditions, with a response time of less than one second. The approach is cost-effective, non-invasive, and easily implementable on contemporary vehicles, ensuring it to be a promising solution for improving road safety.
DOI: https://doi.org/10.5281/zenodo.20066634
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