Drowsy Driving Using OpenCV And Cloud Integration Sing Twilio Api

Driving while feeling drowsy is a cause of accidents on the road. It poses a serious safety risk. This study suggests a system that uses cues to detect driver drowsiness and reduce this danger. The system relies on a trained machine learning model to analyze the driver’s expressions in a video feed. By tracking eye movements and calculating the Eye Aspect Ratio (EAR) across frames, it can identify signs of drowsiness when the EAR drops below a threshold for an extended period, triggering an alarm. In addition, the system can send alerts and make automated calls to the driver’s phone using Twilio. To further enhance safety measures, it also activates the car's hazard lights automatically and shows coffee shops on its interface to help keep drivers alert. This comprehensive approach provides real-time detection of drowsiness and timely interventions, potentially preventing accidents and saving lives on the road.