Development of a 5G-Connected Ultra-Wideband Radar Platform for Traffic Monitoring in a Campus Environment

This paper presents the design, implementation, and testing of a traffic monitoring platform based on 5G-connected Ultra-Wideband (UWB) radars deployed on a university campus. The development of both connected radars and an IoT platform is detailed. The connected radars integrate commercial components, including a Raspberry Pi (RPi), a UWB radar, a standard enclosure, and a custom communication board featuring a 5G module. The IoT platform, which receives data from the radars via MQTT, is scalable, easily deployable, and supports radar management, data visualization, and external data access via an API. The solution was deployed and tested on campus, demonstrating real-time operation over a commercial 5G network with an estimated median latency between the radar and server of 75 ms. A preliminary evaluation conducted on a single radar during peak-hour traffic on a double-lane road, representing a challenging scenario, indicated a high detection rate of 94.81%, a low false detection rate of 1.02%, a high classification accuracy of 97.29%, and a high direction accuracy of 99.66%. These results validate the system's capability to deliver accurate traffic monitoring.

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Publication Details

Journal
Sensors
Published
2025-05-20
DOI
https://doi.org/10.3390/s25103203
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
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Development of a 5G-Connected Ultra-Wideband Radar Platform for Traffic Monitoring in a Campus Environment

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Non-Invasive Vital Sign Monitoring
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Development of a 5G-Connected Ultra-Wideband Radar Platform for Traffic Monitoring in a Campus Environment

David Martín‐Sacristán, Carlos Ravelo, Manuel Fuentes, Mauricio Ortiz, Pablo Trelis
article en

Abstract

This paper presents the design, implementation, and testing of a traffic monitoring platform based on 5G-connected Ultra-Wideband (UWB) radars deployed on a university campus. The development of both connected radars and an IoT platform is detailed. The connected radars integrate commercial components, including a Raspberry Pi (RPi), a UWB radar, a standard enclosure, and a custom communication board featuring a 5G module. The IoT platform, which receives data from the radars via MQTT, is scalable, easily deployable, and supports radar management, data visualization, and external data access via an API. The solution was deployed and tested on campus, demonstrating real-time operation over a commercial 5G network with an estimated median latency between the radar and server of 75 ms. A preliminary evaluation conducted on a single radar during peak-hour traffic on a double-lane road, representing a challenging scenario, indicated a high detection rate of 94.81%, a low false detection rate of 1.02%, a high classification accuracy of 97.29%, and a high direction accuracy of 99.66%. These results validate the system's capability to deliver accurate traffic monitoring.

SensorsVol. 25(10)
Universitat Politècnica de València (ES)
Openalex Percentile: Top 90%
Non-Invasive Vital Sign Monitoring
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