Building Strategic Conformal Automation for Air Traffic Control Using Machine Learning

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Acceptance of automation has been a bottleneck for successful introduction of automation in Air Trac Control. Strategic conformal automation has been proven to increase automation acceptance, by creating a better match between automation and operator decision-making. In this paper strategic conformal automation for Air Trac Control is designed using machine learning techniques. Rather than having pre-dened control strategies, which do not always match with individual operator decision-making, the automation is based on the operator's decision-making. Results show that when operators demonstrate
their control strategies, machine learning techniques can identify these strategies and use them to learn similar control strategies. Apart from mimicking control strategies in iden tical trac scenarios is it possible to use machine learning to solve similar, yet dierent con icts by applying similar control strategies, without the need of human demonstrations for that particular conict scenario. Future research should be done to investigate whether strategic conformal automation indeed increases automation acceptance, as well to investigate how the approach taken in this study can be applied to real-life trac scenarios.
Original languageEnglish
Title of host publicationProceedings of the 2018 AIAA Information Systems-AIAA Infotech @ Aerospace
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages22
ISBN (Electronic)978-1-62410-527-2
Publication statusPublished - 2018
EventAIAA Information Systems-AIAA Infotech at Aerospace, 2018 - Kissimmee, United States
Duration: 8 Jan 201812 Jan 2018


ConferenceAIAA Information Systems-AIAA Infotech at Aerospace, 2018
CountryUnited States
Internet address

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