Smart Embedded System for Accident Pre-Alert and Prevention Using Machine Learning

Road traffic accidents remain a significant global concern, necessitating innovative solutions for accident prevention. This paper introduces a Smart Embedded System for Accident Pre-Alert and Prevention Using Machine Learning (SESAPP) designed to mitigate road safety risks through real-time accident pre-alerts and prevention measures. SESAPP integrates various sensors, including heart rate, ultrasonic, MEMS, eye blink, and alcohol sensors, along with an Arduino Mega microcontroller for data acquisition and processing. Additionally, the system incorporates GPS and GSM modules for location tracking and communication. Python programming language, coupled with Random Forest and Linear Regression algorithms, is employed for data analysis and accident prediction. Experimental results demonstrate the effectiveness of SESAPP in enhancing road safety by accurately predicting potential accidents and triggering timely interventions.