Towards conformal automation in air traffic control: Learning conflict resolution strategies through behavior cloning

Abstract

A critical factor in achieving conformity of automation tools in performing expert tasks, such as air traffic conflict resolution, is the identification of air traffic controllers’ (ATCOs’) preferences (conflict resolution strategies) and the automation tool’s ability to learn and recommend solutions that incorporate these preferences. We propose a machine learning-based framework to learn and predict ATCOs’ conflict resolution preferences through behavior cloning. This framework is an ensemble of five regressor and classifier models. The conflict resolution data to train the machine learning models was collected from 8 experienced enroute ATCOs. The prediction results demonstrate that the ATCOs’ strategies encoded in the data can be learned by the model with high accuracy for the classification tasks and with low mean absolute error (MAE) for the regression task (for instance, the classification accuracy of above 92.7% for predicting the maneuvering aircraft, MAE for maneuver initiation distance ¡ 5.3 NM, MAE for predicting the heading angle ¡ 5.3°) for the ATCOs’ datasets. A sensitivity analysis performed to test the model robustness demonstrates that the proposed models are robust to up to 7.5% added Gaussian noise (with a mean equal to the value of each feature and varying standard deviation) to the dataset. In addition, we discuss the extent of acceptance of these predictions by the ATCOs through an ATCO acceptance exercise involving two ATCOs who demonstrate different conflict resolution strategies. ATCO A selected the original strategy as one of the resolution preferences for 97% of the scenarios and the predicted strategy as one of the options for 78% of the scenarios. ATCO B selected the conflict resolution strategies depicting ATCO B’s original strategies for 68% of the scenarios. The results from the acceptance exercise demonstrate that the proposed machine learning model can generate ATCO conformal predictions. The presented results and discussions also demonstrate the viability of using behavior cloning with chained predictions to develop individual and group conformal automation assistance tools for ATCOs.