@article {104249,
	title = {A Real-Time Consensus-Free Accident Detection Framework for Internet of Vehicles Using Vision Transformer and EfficientNet},
	journal = {AI },
	volume = {7},
	number = {1},
	year = {2026},
	abstract = {
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		Open AccessArticle
	

	
		A Real-Time Consensus-Free Accident Detection Framework for Internet of Vehicles Using Vision Transformer and EfficientNet
	

	
		by\&nbsp;
	

	
		Zineb Seghir
	

	
		\&nbsp;1,
	

	
		Lyamine Guezouli
	

	
		\&nbsp;2,*,
	

	
		Kamel Barka
	

	
		\&nbsp;1,
	

	
		Djallel Eddine Boubiche
	

	
		\&nbsp;2,*,
	

	
		Homero Toral-Cruz
	

	
		\&nbsp;3,*\&nbsp;and
	

	
		Rafael Mart{\'\i}nez-Pel{\'a}ez
	

	
		\&nbsp;4,5
	

	
		1
	

	
		LaSTIC Laboratory, Computer Science Department, University of Batna 2, Batna 05000, Algeria
	

	
		2
	

	
		Laboratory of Renewable Energy, Energy Efficiency and Smart Systems (LEREESI), Higher National School of Renewable Energies, Environment and Sustainable Development (HNS-RE2SD), Batna 05000, Algeria
	

	
		3
	

	
		Departamento de Ingenier{\'\i}a y Tecnolog{\'\i}a, Universidad Aut{\'o}noma del Estado de Quintana Roo, Chetumal 77019, Mexico
	

	
		4
	

	
		Unidad Acad{\'e}mica de Computaci{\'o}n, Universidad Polit{\'e}cnica de Sinaloa, Mazatl{\'a}n 82199, Mexico
	

	
		5
	

	
		Departamento de Ingenier{\'\i}a de Sistemas y Computaci{\'o}n, Universidad Cat{\'o}lica del Norte, Antofagasta 1270709, Chile
	

	
		*
	

	
		Authors to whom correspondence should be addressed.
	

	
		AI\&nbsp;2026,\&nbsp;7(1), 4;\&nbsp;https://doi.org/10.3390/ai7010004
	

	
		Submission received: 12 November 2025\&nbsp;/\&nbsp;Revised: 14 December 2025\&nbsp;/\&nbsp;Accepted: 16 December 2025\&nbsp;/\&nbsp;Published: 22 December 2025
	

	
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				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}
}
