International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Driver drowsiness is a major cause of road accidents, resulting in severe injuries and fatalities. This project proposes a Smart Driver Drowsiness Detection System using Raspberry Pi and advanced Object Detection (ADAS & CAOA) algorithms to monitor driver alertness in real time. The system employs Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to analyse eye closure, yawning, and head movements through continuous camera monitoring. When fatigue is detected, it triggers alerts, activates hazard lights, or sends SOS messages. Using Virtual Network Computing (VNC) for remote monitoring, the system enhances road safety with an intelligent, scalable, and cost-effective solution.

Keywords: Eye Aspect Ratio (EAR), Virtual Network Computing (VNC), Mouth Aspect Ratio (MAR), Object Detection (ADAS & CAOA).

Downloads: | DOI: 10.17148/IJARCCE.2025.141186