<![CDATA[Drowsiness-related traffic accidents are one of the leading causes of road fatalities. Drowsiness reduces concentration and slows a driver’s reaction time to road conditions. This research aims to design and implement a drowsiness detection system based on the Raspberry Pi 4 Model B+, capable of providing early warnings through an audible alarm and sending notifications to an administrator via email. The system employs a web camera to capture real-time facial images of the driver, which are then processed using the Eye Aspect Ratio (EAR) method with the OpenCV and Dlib libraries. If the EAR value falls below a predefined threshold for a certain duration, the system triggers a speaker alarm and sends notifications to the administrator. The system was tested under various lighting and distance conditions to evaluate its accuracy. The results show that the system can detect drowsy eye conditions with an accuracy good. This system is expected to serve as a preventive solution to reduce the risk of accidents caused by drowsiness, particularly for both private and commercial vehicle drivers.]]>
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