Machine learning based visual navigation system architecture for AAM operations with a discussion on its certifiability

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dc.contributor.authorEscudero, Naiara
dc.contributor.authorCostas, Pablo
dc.contributor.authorHardt, Michael W.
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2022-06-30T09:51:04Z
dc.date.available2022-06-30T09:51:04Z
dc.date.issued2022-05-12
dc.description.abstractAdvanced Air Mobility (AAM) is expected to revolutionize the future of general transportation expanding the conventional notion of air traffic to include several services carried out by autonomous aerial platforms. However, the significant challenges associated with such complex scenarios require the introduction of sophisticated technologies able to deliver the resilience, robustness, and accuracy needed to achieve safe, autonomous operations [39]. In this context, solutions based on Artificial Intelligence (AI), able to overcome some limitations found in traditional approaches, are becoming a major opportunity for the aviation industry, but, at the same time, a significant challenge with respect to the certification standards.With the focal point on further proposing a certifiable architecture for AI-enhanced vision navigation in AAM operations, this paper first, summarizes the current technologies and fusion methods applied to date to navigation purposes, to later address the certification problem. Regarding certification, it explores three specific points: 1) traditional certification procedures; 2) current status of AI homologation recommendations; and 3) other certification factors to be considered for future discussion.en_UK
dc.identifier.citationEscudero N, Costas P, Hardt MW, Inalhan G. (2022) Machine learning based visual navigation system architecture for AAM operations with a discussion on its certifiability. In: 2022 Integrated Communication, Navigation and Surveillance Conference (ICNS), 5-7 April 2022, Dulles, VA, USAen_UK
dc.identifier.eissn2155-4951
dc.identifier.isbn978-1-6654-8420-6
dc.identifier.urihttps://doi.org/10.1109/ICNS54818.2022.9771519
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18101
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleMachine learning based visual navigation system architecture for AAM operations with a discussion on its certifiabilityen_UK
dc.typeConference paperen_UK

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