Assuring safe and efficient operation of UAV using explainable machine learning

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dc.contributor.authorAlharbi, Abdulrahman
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorPanagiotakopoulos, Dimitrios
dc.date.accessioned2023-07-13T09:21:39Z
dc.date.available2023-07-13T09:21:39Z
dc.date.issued2023-05-19
dc.description.abstractThe accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements.en_UK
dc.identifier.citationAlharbi A, Petrunin I, Panagiotakopoulos D. (2023) Assuring safe and efficient operation of UAV using explainable machine learning. Drones, Volume 7, Issue 5, May 2023, Article number 327en_UK
dc.identifier.issn2504-446X
dc.identifier.urihttps://doi.org/10.3390/drones7050327
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19978
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectdemand-capacity managementen_UK
dc.subjectexplainable artificial intelligenceen_UK
dc.subjectlow-altitude airspace operationsen_UK
dc.subjectmachine learningen_UK
dc.subjecttraffic-flow managementen_UK
dc.titleAssuring safe and efficient operation of UAV using explainable machine learningen_UK
dc.typeArticleen_UK

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