Conformal automation for air traffic control using convolutional neural networks

Conformal automation for air traffic control using convolutional neural networks

van Rooijen, S. J. (Student TU Delft)
Ellerbroek, Joost (TU Delft Control & Simulation)
Borst, C. (TU Delft Control & Simulation)
van Kampen, E. (TU Delft Control & Simulation)

2019

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.

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

http://resolver.tudelft.nl/uuid:ff1a226c-dffd-492c-9d0a-2d24c507990f

Proceedings of the 13th USA/Europe Air Traffic Management Research and Development Seminar 2019, ATM 2019: 17/06/19 - 21/06/19 Vienna, Austria

13th USA/Europe Air Traffic Management Research and Development Seminar 2019, ATM 2019, 2019-06-17 → 2019-06-21, Vienna, Austria

Institutional Repository

conference paper

© 2019 S. J. van Rooijen, Joost Ellerbroek, C. Borst, E. van Kampen