Embedded based Smart Accident Pre-Alert and Prevention System with Machine Learning

Abstract:-

This project introduces a versatile system that interfaces an Arduino with a USB camera, Machine learning, MQ-3 alcohol sensor, and MEMS sensors to enhance driver safety and accident detection. The USB camera is employed to continuously monitor the driver's eyes through the Machine learning with connected to laptop and communicate with serial for signs of drowsiness, such as eyelid movement indicative of sleepiness. When drowsiness is detected, a buzzer and LED alert are triggered to notify the driver, helping prevent potential accidents. Additionally, the MQ-3 alcohol sensor is integrated into the system to detect alcohol levels. If alcohol is detected on the driver's breath, the system activates both a buzzer and LED alert to ensure the driver is aware of their impairment. When the driver returns to a normal state, these alerts cease.

Furthermore, the project includes MEMS sensors to detect sudden movements or vehicles position or accidents. When the MEMS sensor registers a significant shift in position, indicating an accident change in vehicle direction, the system uses GSM and GPS capabilities to send an SMS containing the vehicle's latitude and longitude coordinates to a designated recipient. This feature enables prompt response and assistance in the event of an accident, potentially saving lifes and reducing response time. Overall, this integrated system not only enhances driver safety by monitoring drowsiness and alcohol impairment but also contributes to accident detection and rapid emergency communication, making it a valuable tool for road safety.

Keywords: Arduino, Accident Detection, MEMS Sensor