IoT- Based Drowsiness Monitoring and Crash Detection System to Enhance Road Safety

Abstract

This project is a solution designed to address the critical issue of driver drowsiness and enhance road safety. The system leverages the power of the Internet of Things (IoT) to continuously monitor driver alertness and promptly detect potential accidents. It comprises a network of sensors strategically placed within the vehicle, including facial recognition cameras and accelerometers. These sensors work together to capture and analyze a driver's physiological and behavioural data in real-time. Facial recognition technology detects signs of drowsiness such as drooping eyelids and yawning. Through a secure IoT connection, this data is transmitted to a central processing unit within the vehicle. Advanced algorithms process this information, assessing the driver's state of alertness. In the event of detected drowsiness, the system initiates a series of proactive interventions, such as sounding alarms, vibrating the driver's seat, or even communicating with an external monitoring centre for further assistance. Moreover, the system's crash detection capabilities are enabled by the accelerometers, which monitor sudden changes in the vehicle's motion. If a collision or accident is detected, the system promptly sends an alert, including the vehicle's GPS coordinates, to emergency services or designated contacts, facilitating rapid response and potentially saving lives. Keywords:- Drowsiness, Crash Detection, Raspberry Pi, IoT