SMART DRIVER DROWSINESS DETECTION AND AUTOMATED EMERGENCY COMMUNICATION SYSTEM

Abstract

Road traffic accidents caused by driver fatigue continue to be a major public safety concern, highlighting the need for intelligent and real-time monitoring systems. This paper presents a Smart Driver Drowsiness Detection and Advanced Emergency Communication System that combines computer vision, deep learning, and automated emergency response to enhance driver safety. The proposed system employs MediaPipe Face Mesh to accurately detect facial landmarks and localize the driver’s eye regions from live video captured through a standard webcam. A Convolutional Neural Network (CNN) is utilized to classify the eye state as open or closed, while an adaptive eyescore mechanism continuously evaluates the driver’s alertness by analyzing consecutive eye-closure patterns. When signs of prolonged drowsiness are detected, the system immediately activates an audible warning to regain the driver’s attention. If the driver fails to respond after multiple warning cycles, the system initiates an automated emergency communication process by obtaining the current GPS location, generating a Google Maps navigation link, transmitting an emergency email to predefined contacts, and placing an emergency phone call using Android Debug Bridge (ADB). Unlike conventional driver monitoring systems that focus solely on fatigue detection, the proposed framework integrates an intelligent emergency response mechanism to provide timely assistance during critical situations. The system is entirely nonintrusive, operates in real time using inexpensive hardware, and does not require wearable sensors, making it practical for deployment in modern vehicles. Experimental evaluation demonstrates that the integration of MediaPipe and CNN enables reliable eyestate recognition with low computational complexity, providing an effective solution for reducing fatigue-related accidents and improving road safety.