Framework demonstrates end-to-end latency under 0.02 s in smart cities, suggesting advancements in intelligent transportation systems and data management.
This paper presents a framework that integrates real-time onboard (ego vehicle) perception module with edge processing capabilities and a cloud-based digital twin for intelligent transportation systems (ITSs) in smart city applications. The proposed system combines onboard 3D object detection and tracking with low latency edge-to-cloud communication, achieving an average end-to-end latency below 0.02 s at 10 Hz update frequency. Experiments conducted on a real autonomous vehicle platform demonstrate a mean Average Precision (mAP@40) of 83.5% for the 3D perception module. The proposed system enables real-time traffic visualization while enabling scalable data management by reducing communication overhead. Future work will extend the system to multi-vehicle deployments and incorporate additional environmental semantics such as traffic signal states, road conditions, and predictive Artificial Intelligence (AI) models to enhance decision support in dynamic urban environments.
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Iqbal et al. (2025) studied this question.
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