Drowsiness and Crash Detection Mobile Application for Vehicle’s Safety

Abstract

Detecting road accidents promptly is crucial for minimizing casualties and property damage worldwide. The proposed system, comprising hardware and a mobile application, automatically identifies and reports accidents to emergency services. It also employs a facial recognition system to detect driver drowsiness, enhancing accident prevention measures. By leveraging sensor technologies, cellular networks, and advanced detection algorithms, the proposed system analyzes data from accelerometers, Global System for Mobile Communication (GSM), and Global Positioning System (GPS) sensors. Originally designed for vehicles, it can be easily adapted for deployment in various settings such as factories and construction sites with minor adjustments. The system continuously monitors the driver's facial expressions and activities using sensors. When drowsiness is detected, it activates a buzzer, and in the event of a crash, it alerts the driver to prevent false alarms while simultaneously notifying the rescue center if a genuine crash has occurred. This integrated approach enhances safety and optimizes emergency response efforts. The Arduino microcontroller, equipped with an accelerometer, identifies sudden changes in motion like acceleration and rotation to assess impacts against predefined thresholds. Furthermore, GPS functionality accurately determines the vehicle's location at the time of the accident, while GSM enables seamless communication with rescue centers through notifications.