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Communication Dans Un Congrès Année : 2023

Learning Uncertainty Parameters for Assistance in Conflict Resolution

Sarah Degaugue
  • Fonction : Auteur
  • PersonId : 1120687
Richard Alligier
Jean-Baptiste Gotteland
Nicolas Durand

Résumé

Helping Air Traffic Controllers (ATCOs) to solve conflicts is challenging because ATCOs only have a partial control on pilots reaction time and trajectory change, and cannot estimate very precisely the aircraft speed. A previous research [1] proposed a method to estimate ATCOs' uncertainty margins during their deconfliction task. It was shown that, given a predefined uncertainty model, it is possible to learn uncertainty parameters on two-aircraft exercises resolved by an automatic solver. In this article, we collect new data on a more realistic simulator showing a Singaporean En-Route sector and estimate individual and collective uncertainties. These uncertainties are then used in the automatic solver and the resolutions are compared to the actual maneuvers given by the ATCOs. Results on 6 ATCOs who performed several hours of control show that common uncertainties could be estimated with an error of the same range as individual uncertainties. When these uncertainties are used in the automatic solver the solutions are conform to the ATCOs decisions 77 per cent of the time which is 15 percent higher than without considering uncertainties. ACKNOWLEDGEMENT We thank the ATCOs from ENAC who volunteered to participate in our experiments, giving several hours of their time. Their involvement is essential for collecting relevant data.
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Dates et versions

hal-04357427 , version 1 (21-12-2023)

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  • HAL Id : hal-04357427 , version 1

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Sarah Degaugue, Yash Guleria, Richard Alligier, Jean-Baptiste Gotteland, Nicolas Durand. Learning Uncertainty Parameters for Assistance in Conflict Resolution. USA-Europe ATM Research and Development Seminar 2023, FAA-Eurocontrol, Jun 2023, SAVANNAH, United States. ⟨hal-04357427⟩

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