Near-Miss Accident Prediction on the Edge: A Real-Time System for Safer Driving

This paper presents an innovative approach to predicting near-miss accidents, vital for proactive accident prevention. Leveraging dashcam footage and weather sensor data, our model integrates camera calibration, collision point prediction, heuristic knowledge, and analysis of near-miss accident patterns. We propose a comprehensive method to detect and predict potential collisions within the ego-vehicle's safe zone, utilizing a combination of machine learning techniques including DeepHough, YOLOv8, and LSTM. Furthermore, we introduce heuristic rules to handle sudden changes in object behavior and enhance object detection accuracy under challenging conditions like low visibility. Our approach identifies common near-miss accident patterns and achieves a prediction accuracy of 96.01% with support from hard brake detection. Comparative analysis demonstrates the superior performance of our method against existing benchmarks. Moreover, our lightweight model is designed for deployment on edge clients, ensuring real-time assistance to drivers. Collaboratively developed with government and industry stakeholders, our approach contributes to creating cost-effective smart driving assistance systems with wide-ranging applications in traffic safety and accident analysis.