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

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.