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Aerospace 2018, 5(4), 103; https://doi.org/10.3390/aerospace5040103

Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification

1
Technical Directorate, Thales Air Traffic Management, Melbourne, VIC 3001, Australia
2
School of Engineering, Aerospace Engineering and Aviation, RMIT University, Bundoora, VIC 3083, Australia
*
Author to whom correspondence should be addressed.
Received: 6 August 2018 / Revised: 21 September 2018 / Accepted: 26 September 2018 / Published: 1 October 2018
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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–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. View Full-Text
Keywords: cognitive HMI; machine learning; explainable artificial intelligence; computer-aided verification; air traffic management cognitive HMI; machine learning; explainable artificial intelligence; computer-aided verification; air traffic management
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kistan, T.; Gardi, A.; Sabatini, R. Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification. Aerospace 2018, 5, 103.

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