Trajectory prediction (TP) plays an important role in supporting the
decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are
deterministic and physics-based, with parameters that are calibrated using
aircraft surveillance data harvested across the world. These models are,
therefore, agnostic to the intentions of the pilots and ATCOs, which can have a
significant effect on the observed trajectory, particularly in the lateral
plane. This work proposes a generative method for lateral TP, using
probabilistic machine learning to model the effect of the epistemic uncertainty
arising from the unknown effect of pilot behaviour and ATCO intentions. The
models are trained to be specific to a particular sector, allowing local
procedures such as coordinated entry and exit points to be modelled. A dataset
comprising a week's worth of aircraft surveillance data, passing through a busy
sector of the United Kingdom's upper airspace, was used to train and test the
models. Specifically, a piecewise linear model was used as a functional,
low-dimensional representation of the ground tracks, with its control points
determined by a generative model conditioned on partial context. It was found
that, of the investigated models, a Bayesian Neural Network using the Laplace
approximation was able to generate the most plausible trajectories in order to
emulate the flow of traffic through the sector.

该研究提出了一种概率机器学习的生成方法，用于侧面轨迹预测，旨在建模来自飞行员行为和空中交通管制员意图的认知不确定性对轨迹的影响，探索了不同模型中贝叶斯神经网络与拉普拉斯近似的概率方法在模拟飞机通过空域的流量方面的可行性。

基于上下文的生成模型用于飞机地面轨迹预测

Context-Aware Generative Models for Prediction of Aircraft Ground Tracks

This paper describes a simple yet efficient repetition-based modular system
for speeding up air-traffic controllers (ATCos) training. E.g., a human pilot
is still required in EUROCONTROL's ESCAPE lite simulator (see
this https URL) during ATCo training. However,
this need can be substituted by an automatic system that could act as a pilot.
In this paper, we aim to develop and integrate a pseudo-pilot agent into the
ATCo training pipeline by merging diverse artificial intelligence (AI) powered
modules. The system understands the voice communications issued by the ATCo,
and, in turn, it generates a spoken prompt that follows the pilot's phraseology
to the initial communication. Our system mainly relies on open-source AI tools
and air traffic control (ATC) databases, thus, proving its simplicity and ease
of replicability. The overall pipeline is composed of the following: (1) a
submodule that receives and pre-processes the input stream of raw audio, (2) an
automatic speech recognition (ASR) system that transforms audio into a sequence
of words; (3) a high-level ATC-related entity parser, which extracts relevant
information from the communication, i.e., callsigns and commands, and finally,
(4) a speech synthesizer submodule that generates responses based on the
high-level ATC entities previously extracted. Overall, we show that this system
could pave the way toward developing a real proof-of-concept pseudo-pilot
system. Hence, speeding up the training of ATCos while drastically reducing its
overall cost.

本文介绍了一种简单而高效的基于重复的模块化系统，以加速空中交通管制员的培训。该系统通过融合不同的人工智能技术，开发和集成一种伪飞行员代理，实现了对管制员语音通讯的理解，并生成响应以提高其训练效率。

伪飞行模拟器的语音和自然语言处理技术

Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator

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.

本研究中，我们提出深度学习方法来学习空中交通管理领域的交通管制员解决冲突的反应，以增强冲突检测和解决任务的自动化，该方法通过预测可能违反飞机航迹之间的最小分隔约束的冲突解决时刻的轨迹点，来评估其准确性。