@article {104303,
	title = {A Real-Time Consensus-Free Accident Detection Framework for Internet of Vehicles Using Vision Transformer and EfficientNet},
	journal = {AI (Switzerland)},
	volume = {7},
	number = {1},
	year = {2026},
	abstract = {
	Objectives: Traffic accidents cause severe social and economic impacts, demanding fast and reliable detection to minimize secondary collisions and improve emergency response. However, existing cloud-dependent detection systems often suffer from high latency and limited scalability, motivating the need for an edge-centric and consensus-free accident detection framework in IoV environments.\&nbsp;



	Methods: This study presents a real-time accident detection framework tailored for Internet of Vehicles (IoV) environments. The proposed system forms an integrated IoV architecture combining on-vehicle inference, RSU-based validation, and asynchronous cloud reporting. The system integrates a lightweight ensemble of Vision Transformer (ViT) and EfficientNet models deployed on vehicle nodes to classify video frames. Accident alerts are generated only when both models agree (vehicle-level ensemble consensus), ensuring high precision. These alerts are transmitted to nearby Road Side Units (RSUs), which validate the events and broadcast safety messages without requiring inter-vehicle or inter-RSU consensus. Structured reports are also forwarded asynchronously to the cloud for long-term model retraining and risk analysis.\&nbsp;



	Results: Evaluated on the CarCrash and CADP datasets, the framework achieves an F1-score of 0.96 with average decision latency below 60 ms, corresponding to an overall accuracy of 98.65\% and demonstrating measurable improvement over single-model baselines.\&nbsp;



	Conclusions: By combining on-vehicle inference, edge-based validation, and optional cloud integration, the proposed architecture offers both immediate responsiveness and adaptability, contrasting with traditional cloud-dependent approaches.

},
	url = {https://doi.org/10.3390/ai7010004},
	author = {Seghir,  Zineb and Lyamine Guezouli and Kamel Barka and Boubiche, Djallel-Eddine and Homero Toral-Cruz and Mart{\'\i}nez-Pel{\'a}ez, Rafael}
}
