Driver Drowsiness Detection using Novel Deep Learning
Keywords:
Deep learning, Driver Drowsiness, Machine Learning, Artificial IntelligenceAbstract
Road accidents remain one of the leading causes of death worldwide, with driver drowsiness identified as a major contributing factor, particularly during long-distance travel. Fatigue reduces a driver’s alertness, reaction time, and decision-making ability, significantly increasing the risk of accidents. Therefore, developing an intelligent system capable of detecting driver drowsiness in real time is essential to enhance road safety. Artificial Intelligence (AI), particularly deep learning, has emerged as a promising technological approach to address this issue due to its strong capability in image processing and facial recognition tasks. This study focuses on the development of a deep learning–based driver drowsiness detection system using facial features as the primary indicators of fatigue. The proposed approach involves collecting and preparing a dataset consisting of driver facial images under various conditions, including normal and drowsy states. Key facial indicators such as eye closure, blinking frequency, and yawning patterns are analyzed to identify signs of drowsiness. A convolutional neural network (CNN) architecture is then employed to learn these visual patterns and classify the driver’s state into alert or drowsy categories. The system is designed to operate in real time using a camera-based monitoring mechanism that continuously captures facial data while the driver is operating the vehicle. When signs of drowsiness are detected, the system can trigger an alert to warn the driver and prevent potential accidents. The results demonstrate that deep learning models are highly effective in recognizing fatigue-related facial patterns, providing a reliable solution for intelligent driver monitoring systems. This research contributes to the advancement of AI-based safety technologies aimed at reducing traffic accidents and improving transportation safety.
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Copyright (c) 2025 Bhupesh Rawat, Untung Rahardja, Marviola Hardini, Aulia Rahma Dina
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