Abstract:Digital twins (DTs) for urban transportation systems have gained increasing attention; however, their systematic evaluation in safety-critical scenarios remains limited. This paper presents a multi-pedestrian safety warning system at urban intersections enabled by a tightly coupled physical-digital twin framework. Built upon the COSMOS city-scale wireless testbed in New York City, the proposed system integrates camera and ultra-wideband (UWB), edge-cloud computing, predictive trajectory modeling, and MQTT-based communication to deliver real-time safety alerts to vulnerable road users (VRUs). The system is evaluated through both field deployment and virtual reality (VR) experiments. Results demonstrate high warning generation accuracy, localization accuracy, efficient end-to-end latency under different model configurations, and significant reductions in user response time when warnings are issued. The proposed DT framework provides a scalable, modular, and generalizable solution for real-time multi-pedestrian safety enhancement at complex urban intersections.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.18823 [cs.LG] |
| (or arXiv:2605.18823v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18823 arXiv-issued DOI via DataCite (pending registration) |
From: Yongjie Fu [view email]
[v1]
Tue, 12 May 2026 16:28:49 UTC (2,721 KB)
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