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Evaluation of Coordination Strategies for Underground Automated Vehicle Fleets in Mixed Traffic
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0009-0001-7403-9691
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-9607-9504
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-3122-693X
2025 (English)In: 2025 IEEE Intelligent Vehicles Symposium (IV): Proceedings, IEEE, 2025, p. 1020-1026Conference paper, Published paper (Refereed)
Abstract [en]

This study investigates the efficiency and safety outcomes of implementing different adaptive coordination models for automated vehicle (AV) fleets, managed by a centralized coordinator that dynamically responds to human-controlled vehicle behavior. The simulated scenarios replicate an underground mining environment characterized by narrow tunnels with limited connectivity. To address the unique challenges of such settings, we propose a novel metric — Path Overlap Density (POD) — to predict efficiency and potentially the safety performance of AV fleets. The study also explores the impact of map features on AV fleets performance. The results demonstrate that both AV fleet coordination strategies and underground tunnel network characteristics significantly influence overall system performance. While map features are critical for optimizing efficiency, adaptive coordination strategies are essential for ensuring safe operations. 

Place, publisher, year, edition, pages
IEEE, 2025. p. 1020-1026
Series
IEEE Intelligent Vehicles Symposium (IV), ISSN 1931-0587, E-ISSN 2642-7214
Keywords [en]
Mixed traffic with fleets of automated vehicles, path overlap density (POD), human behavior in driving, centralized coordination, coordination strategies, underground mining
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-122686DOI: 10.1109/IV64158.2025.11097685ISI: 001556907500146ISBN: 9798331538033 (electronic)ISBN: 9798331538040 (print)OAI: oai:DiVA.org:oru-122686DiVA, id: diva2:1987729
Conference
36th IEEE Intelligent Vehicles Symposium (IEEE IV2025), Cluj-Napoca, Romania, June 22-25, 2025
Funder
Knowledge Foundation, 20190128
Note

This work has been supported by Sustainable Underground Mining (SUM) Academy, Project SP-12 2021-2024, and the Industrial Graduate School Collaborative AI & Robotics funded by the Swedish Knowledge Foundation Dnr:20190128. 

Available from: 2025-08-07 Created: 2025-08-07 Last updated: 2025-10-02Bibliographically approved

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Mironenko, OlgaBanaee, HadiLoutfi, Amy

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