<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Seghir,  Zineb</style></author><author><style face="normal" font="default" size="100%">Lyamine Guezouli</style></author><author><style face="normal" font="default" size="100%">Kamel Barka</style></author><author><style face="normal" font="default" size="100%">Boubiche, Djallel-Eddine</style></author><author><style face="normal" font="default" size="100%">Homero Toral-Cruz</style></author><author><style face="normal" font="default" size="100%">Martínez-Peláez, Rafael</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Real-Time Consensus-Free Accident Detection Framework for Internet of Vehicles Using Vision Transformer and EfficientNet</style></title><secondary-title><style face="normal" font="default" size="100%">AI (Switzerland)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.3390/ai7010004</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	&lt;b&gt;Objectives&lt;/b&gt;: 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.&amp;nbsp;
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	&lt;b&gt;Methods&lt;/b&gt;: 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.&amp;nbsp;
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	&lt;b&gt;Results&lt;/b&gt;: 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.&amp;nbsp;
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	&lt;b&gt;Conclusions&lt;/b&gt;: 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.
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