The quick advancement of Autonomous Vehicular Networks (AVNs) shows remarkable potential to transform urban transportation systems in smart cities. This transformational process faces several crucial issues, primarily due to the lack of clear decision-making, concerns about public confidence, and the need for prompt protective measures in various contexts. The implementation of AVNs depends on resolving current adversities, as these difficulties affect both system safety, user trust, and performance reliability. Traditional AVN development focused on enhancing technical capabilities, such as reliability, but failed to adequately address issues with decision transparency and interpretability. The current systems fall short because they lack an understanding of how Autonomous Vehicles (AVs) generate decisions in real-time urban conditions, which impedes public confidence and broader adoption. To address these limitations, this study integrates You Only Look Once, V5 (YOLOv5), a fast and lightweight object detection model well-suited for AVs, alongside Explainable AI (XAI) techniques to ensure interpretability and transparency. In this research, an XAI-based YOLOv5 model is proposed to enable real-time, explainable decision-making. Its objectives are to increase transparency, increase safety, and gain public acceptance for connecting the AVNs to smart city systems. The proposed model achieves an accuracy of 99% with a miss rate of 1%, thereby enhancing classification accuracy and public confidence. The proposed work also aims to foster public trust in AVNs within smart city ecosystems by making AI decisions more transparent and interpretable.
Keywords: Autonomous vehicular networks (AVNs); Explainable AI (XAI); Infrastructure-to-Infrastructure (I2I); Vehicle-to-Infrastructure (V2I); Vehicle-to-Vehicle (V2V).
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