An efficient computer vision framework for RSU-based accident recognition and V2X communication

Abstract

Road traffic accidents require rapid detection and timely warning dissemination to reduce secondary collisions and improve emergency response. This paper presents an efficient roadside-unit-based computer vision framework for real-time accident recognition integrated with Vehicle-to-Everything communication. The proposed architecture combines lightweight convolutional spatial feature extraction, optical-flow-based motion analysis, temporal score aggregation, and low-latency V2X alert generation within an edge-deployable RSU pipeline. A formal system model is introduced to describe accident likelihood estimation, detection decision making, latency constraints, and communication reliability requirements. The experimental evaluation was conducted on a traffic video dataset containing 3,300 clips with normal traffic, single-vehicle accidents, multi-vehicle collisions, and non-accident anomalies. The proposed hybrid framework achieved 95.8 % accuracy, 95.0 % precision, 94.6 % recall, and 94.8 % F1-score, outperforming CNN-only and motion-only baselines. The average processing latency was approximately 40 ms per frame, indicating the feasibility of real-time operation on RSU-grade embedded hardware. These results show that the proposed framework can provide accurate and timely accident detection while supporting rapid V2X warning dissemination for next-generation intelligent transportation systems.