Validation of a UAV-Based Digital Twin for VRU Safety in Urban Environments with Simulations

Abstract

Vulnerable Road Users (VRUs), including pedestrians, cyclists and micromobility users, remain exposed to high risk in urban traffic locations where buildings, parked vehicles or infrastructure create occlusions. This paper validates a UAV-based digital twin for VRU safety in urban environments using a combined field-test and simulation-supported methodology. The digital twin is designed as a temporary and rapidly deployable perception extension for connected and automated vehicles. The proposed Physical, Digital and Communication infrastructure combines a UAV-mounted camera, edge-based artificial intelligence, a roadside unit and the vehicle onboard unit. The UAV monitors a region of interest and streams video to the edge node, where VRUs are detected, tracked and projected into world coordinates using RTK-enabled georeferencing and camera pose information. Vehicle states are received through Cooperative Awareness Messages, while the edge node uses synchronized state estimation and short-horizon trajectory prediction to assess possible path conflicts and time-to-collision. When a relevant risk is identified, warning information is inserted into the vehicle perception loop through standardized V2X messages. The validation was performed through pedestrian-crossing trials at the EMT Carabanchel bus depot in Madrid and through openPASS simulations of the occluded turning geometry. The results show that the UAV-based digital twin can detect occluded pedestrians before they are available to onboard sensors and can support more progressive vehicle deceleration. The simulation comparison confirms that, in the occluded turning scenario, the digital-twin warning provides an operational advantage by enabling a more anticipatory and interpretable vehicle response than vehicle-only AEB. Future research will perform a large number of simulations to vary and explore more scenarios.

Keywords: UAV, Digital Twin, Vulnerable Road Users, Collective Perception, V2X, openPASS, Connected and Automated Vehicles

DOI: 10.54941/ahfe1008129