Machine Learning-Driven Framework for Automated Accident Detection and Risk Prevention

Abstract

Traffic hazards have become a major global concern due to rapid urbanization, dense population growth, and the increasing number of vehicles on roadways. Road accidents are among the leading causes of death worldwide, creating an urgent need for intelligent transportation solutions that can reduce accident rates and save lives. This paper proposes a machine learning-driven framework for automated accident detection and risk prevention using computer vision techniques. The system captures real-time road traffic footage through a camera and processes it using OpenCV to monitor vehicles and detect accident-related events. In addition, machine learning algorithms are applied to analyze traffic patterns and predict the likelihood of accidents before they occur. The framework also includes an alert mechanism to notify nearby emergency services immediately in the event of an accident, enabling faster response and reducing casualties. The proposed approach aims to improve road safety by providing early warnings, enhancing emergency assistance, and supporting the development of smarter transportation systems.