SMART DRIVER SAFETY AND EMERGENCY RESPONSE SYSTEM FOR ROAD ACCIDENT PREVENTION
DOI:
https://doi.org/10.63458/ijerst.v4i1.145Keywords:
Driver Drowsiness Detection, Eye Aspect Ratio, Computer Vision, Embedded Systems, Vehicle SafetyAbstract
Road accidents caused by driver fatigue and sudden medical emergencies remain a critical challenge in intelligent transportation systems. Conventional vehicle safety mechanisms such as airbags and seat belts operate only after a collision has occurred and do not provide proactive accident prevention. To address this limitation, this paper proposes an AI-Powered Driver Health Monitoring and Autonomous Rescue Vehicle System designed to detect driver drowsiness in real time and automatically initiate safety actions. The system employs computer vision techniques using OpenCV and facial landmark detection to compute the Eye Aspect Ratio (EAR) for continuous monitoring of eye closure patterns. When the EAR value falls below a predefined threshold for a sustained duration, the system identifies a drowsy state and triggers automated control signals to a simulated embedded platform. The vehicle motor is stopped, alert messages are displayed, and rescue mechanisms are activated to ensure safety. The proposed architecture integrates artificial intelligence with embedded system control, providing a cost-effective, non-intrusive, and real-time solution for accident prevention. Experimental validation demonstrates reliable fatigue detection with minimal response delay, confirming the feasibility of deploying AI-driven proactive safety systems in next-generation smart vehicles.
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Copyright (c) 2026 M.B.Ramesh, R.Saranraj, C.Sedhuraman, M.Senthilvelan
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