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SUAVPy: A SUMO Plugin for UAV-Based Ground Traffic Sensing

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Názov: SUAVPy: A SUMO Plugin for UAV-Based Ground Traffic Sensing
Autori: Charalambos Tsioutis, Christos Makridis, Stelios Timotheou
Zdroj: SUMO Conference Proceedings, Vol 6 (2025)
Informácie o vydavateľovi: TIB Open Publishing, 2025.
Rok vydania: 2025
Predmety: UAV-Based Sensing, Open-Source Tools, Transportation and communications, Traffic Monitoring, HE1-9990
Popis: In recent years, Unmanned Aerial Vehicles (UAVs) have emerged as effective tools for traffic monitoring and control by offering high-resolution, aerial observations of vehicular movement. Although UAV simulation is well established, tools to capture microscopic traffic measurements from UAV-based observations remain limited. This paper introduces SUMO-UAV-Py, an open-source SUMO plugin that integrates UAV-based sensing into microscopic traffic simulations in Python. SUMO-UAV-Py captures detailed vehicle observations by dynamically employing multiple UAVs to observe traffic measurements based on their position and field-of-view (FoV). Performance evaluations on a mid-sized network demonstrate that SUMO-UAV-Py maintains simulation performance comparable to standard post-processing methods, confirming its suitability for large-scale traffic monitoring research.
Druh dokumentu: Article
ISSN: 2750-4425
DOI: 10.52825/scp.v6i.2610
Prístupová URL adresa: https://doaj.org/article/20b128fb581d47c69d4cabfbaa527b68
Rights: URL: https://creativecommons.org/licenses/by/3.0/de/deed.en
Prístupové číslo: edsair.doi.dedup.....41a95fc3c4845f62d58141a3d784b890
Databáza: OpenAIRE
Popis
Abstrakt:In recent years, Unmanned Aerial Vehicles (UAVs) have emerged as effective tools for traffic monitoring and control by offering high-resolution, aerial observations of vehicular movement. Although UAV simulation is well established, tools to capture microscopic traffic measurements from UAV-based observations remain limited. This paper introduces SUMO-UAV-Py, an open-source SUMO plugin that integrates UAV-based sensing into microscopic traffic simulations in Python. SUMO-UAV-Py captures detailed vehicle observations by dynamically employing multiple UAVs to observe traffic measurements based on their position and field-of-view (FoV). Performance evaluations on a mid-sized network demonstrate that SUMO-UAV-Py maintains simulation performance comparable to standard post-processing methods, confirming its suitability for large-scale traffic monitoring research.
ISSN:27504425
DOI:10.52825/scp.v6i.2610