论文标题
数据驱动的空中交通管制员对解决冲突的反应
Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts
论文作者
论文摘要
With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, in this paper we propose deep learning techniques (DL) that can learn models of Air Traffic Controllers' (ATCO) reactions in resolving conflicts that can violate separation minimum constraints among aircraft trajectories: This implies learning when the ATCO will react towards resolving a conflict, and how he/she will react.本文针对的及时反应着眼于反应何时发生，旨在预测轨迹的轨迹点，即轨迹的发展，即ATCO发出了冲突解决方案行动，同时还可以预测解决方案行动的类型（如果有的话）。为了实现这一目标，本文为CD＆R提出了ATCO反应的预测问题，并提出了可以在现实世界中数据集中对ATCO进行建模并评估这些方法的DL方法，以非常高的精度显示了其预测的功效。
With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, in this paper we propose deep learning techniques (DL) that can learn models of Air Traffic Controllers' (ATCO) reactions in resolving conflicts that can violate separation minimum constraints among aircraft trajectories: This implies learning when the ATCO will react towards resolving a conflict, and how he/she will react. Timely reactions, to which this paper aims, focus on when do reactions happen, aiming to predict the trajectory points, as the trajectory evolves, that the ATCO issues a conflict resolution action, while also predicting the type of resolution action (if any). Towards this goal, the paper formulates the ATCO reactions prediction problem for CD&R, and presents DL methods that can model ATCO timely reactions and evaluates these methods in real-world data sets, showing their efficacy in prediction with very high accuracy.
