Abstract
Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human&ndash
machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems.
Document type: Article
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The different versions of the original document can be found in:
- http://dx.doi.org/10.3390/aerospace5040103 under the license https://creativecommons.org/licenses/by
- http://dx.doi.org/10.3390/aerospace5040103 under the license http://creativecommons.org/licenses/by/3.0/
- https://www.mdpi.com/2226-4310/5/4/103/pdf under the license https://creativecommons.org/licenses/by/4.0
- https://doaj.org/toc/2226-4310 under the license cc-by
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Published on 01/01/2018
Volume 2018, 2018
DOI: 10.3390/aerospace5040103
Licence: Other
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