Demo: Integrating AR headsets with Mobile Object Detection to extend road sensing and VRU safety

This work showcases a framework that integrates Augmented Reality (AR) headsets and mobile object detection to enhance the safety of road users in a smart city environment. The framework leverages AR devices, such as the Microsoft HoloLens 2, as mobile sensing hubs to capture real-time sensor data. By equipping Vulnerable Road Users (VRUs) with these devices, the system enables dynamic sensing and data contribution, addressing the limitations of fixed infrastructure sensors. The proposed architecture employs microservices to ensure modularity and real-time data processing, meeting the latency requirements for safety-related systems, of under 300 ms. The demonstration features real-world tests, showcasing its potential to improve urban safety and mobility applications.

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Publication Details

Published
2025-06-02
DOI
https://doi.org/10.1109/vnc64509.2025.11054108
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
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article

Demo: Integrating AR headsets with Mobile Object Detection to extend road sensing and VRU safety

Susana Sargento, Pedro Rito, Gonçalo Silva, André Clérigo
Advanced Neural Network Applications
article

Demo: Integrating AR headsets with Mobile Object Detection to extend road sensing and VRU safety

Susana Sargento, Pedro Rito, Gonçalo Silva, André Clérigo
article en

Abstract

This work showcases a framework that integrates Augmented Reality (AR) headsets and mobile object detection to enhance the safety of road users in a smart city environment. The framework leverages AR devices, such as the Microsoft HoloLens 2, as mobile sensing hubs to capture real-time sensor data. By equipping Vulnerable Road Users (VRUs) with these devices, the system enables dynamic sensing and data contribution, addressing the limitations of fixed infrastructure sensors. The proposed architecture employs microservices to ensure modularity and real-time data processing, meeting the latency requirements for safety-related systems, of under 300 ms. The demonstration features real-world tests, showcasing its potential to improve urban safety and mobility applications.

Instituto de Telecomunicações (PT), University of Aveiro (PT)
Openalex Percentile: Top 86%
Advanced Neural Network Applications
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