Smart Vehicle Assistance and Accident Prevention System

Abstract

Road accidents remain a leading cause of fatalities worldwide, necessitating intelligent solutions to enhance vehicle safety and accident prevention. This paper presents a Smart Vehicle Assistance and Accident Prevention System that leverages Internet of Things (IoT) and Machine Learning (ML) to minimize road mishaps and assist drivers in real time. The system integrates sensor-based vehicle monitoring, computer vision for object detection, driver behavior analysis, and predictive accident modeling to enhance situational awareness. By continuously assessing environmental factors such as road conditions, traffic congestion and driver fatigue, the proposed system provides real-time alerts and automated interventions to prevent potential collisions. Additionally, an emergency response mechanism ensures immediate accident reporting to authorities, reducing response time and improving survival chances. The research incorporates a deep learning model trained on extensive accident datasets to predict high-risk scenarios, while an interactive dashboard visualizes accident-prone areas and associated risk factors. Experimental results demonstrate the system’s effectiveness in reducing accident probability and enhancing driver awareness. This study highlights the potential of AI-driven vehicular safety and its impact on modern transportation, aiming to make roads safer and more intelligent.