Drowsy Driver - Machine Learning-Based Real-Time Driver Drowsiness Detection

Driver fatigue is a significant factor in road accidents, posing a serious risk to safety. TheDrowsy Driver aims to address this issue by leveraging advanced technologies to monitor driver alertness inreal time. This system integrates computer vision, machine learning, and IoT technologies to detect signsof drowsiness and other risky behaviors such as mobile phone usage while driving.The system employs a webcam interfaced with a Raspberry Pi to capture live video of the driver. Through image processing and facial landmark detection, it identifies signs of fatigue, such as eye closure and head tilt. Upon detecting drowsiness or distractions, the system triggers an immediate buzzer alert inside the vehicle and sends notifications to a mobile application using Firebase. The app displays real-time alerts, GPS location, and detection data, providing timely updates to the driver or their emergency contacts. This system is designed to be robust and adaptable, incorporating additional safety features like SOS triggers and accident detection, creating a comprehensive driver monitoring solution. By enhancing road safety through proactive alerts, the Drowsy Driver contributes to reducing accidents and saving lives. Index Terms—Drowsiness Detection, MachineLearning, Raspberry Pi, AI, Real-Time Monitoring, MediaPipe, Computer Vision.