Conformal automation for air traffic control using convolutional neural networks

S. J. van Rooijen, J. Ellerbroek, C. Borst, E. van Kampen

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

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Abstract

Lack of trust has been identified as an obstacle in the introduction of workload-alleviating automation in air traffic control. The work presented in this paper describes a concept to generate individual-sensitive resolution advisories for air traffic conflicts, with the aim of increasing acceptance by adapting advisories to different controller strategies. These personalized advisories are achieved using a tailored convolutional neural network model that is trained on individual controller data. In this study, a human-in-the-loop experiment was performed to generate datasets of conflict geometries and controller resolutions, with a velocity obstacle representation as a learning feature. Results show that the trained models can reasonably predict command type, direction and magnitude. Furthermore, a correlation is found between controller consistency and achieved prediction performance. A comparison between individual-sensitive and general models showed a benefit of individually trained models, confirming the strategy heterogeneity of the population, which is a critical assumption for personalized automation.

Original languageEnglish
Title of host publicationProceedings of the 13th USA/Europe Air Traffic Management Research and Development Seminar 2019, ATM 2019
Subtitle of host publication17/06/19 - 21/06/19 Vienna, Austria
Number of pages10
Publication statusPublished - 2019
Event13th USA/Europe Air Traffic Management Research and Development Seminar 2019, ATM 2019 - Vienna, Austria
Duration: 17 Jun 201921 Jun 2019

Conference

Conference13th USA/Europe Air Traffic Management Research and Development Seminar 2019, ATM 2019
CountryAustria
CityVienna
Period17/06/1921/06/19

Keywords

  • Consistency
  • Decision-support
  • Machine learning
  • Solution space diagram
  • Strategic conformance
  • Velocity obstacles

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  • Cite this

    van Rooijen, S. J., Ellerbroek, J., Borst, C., & van Kampen, E. (2019). Conformal automation for air traffic control using convolutional neural networks. In Proceedings of the 13th USA/Europe Air Traffic Management Research and Development Seminar 2019, ATM 2019: 17/06/19 - 21/06/19 Vienna, Austria