Detecting Controllers' Actions in Past Mode S Data by Autoencoder-Based Anomaly Detection

International audienceThe preparation and execution of training simulations for Air Traffic Control (ATC) and pilots requires a significant commitment of operational experts. Such a mobilisation could be alleviated by a decision support tool trained to generate a realistic environment based on historical data. Prior to studying methods able to learn from a dataset of traffic patterns and ATC orders observed in the past, we focus here on the constitution of such a database from a history of trajectories: the difficulty lies in the fact that past flown trajectories are properly regulated, that observed situations may depend on a wide range of potentially unknown factors and that ownership rules apply on parts of the data. We present here a method to analyse flight trajectories, detect unusual flight behaviours and infer ATC actions. When an anomaly is detected, we place the trajectory in context, then assess whether such anomaly could correspond to an ATC action. The trajectory outlier detection method is based on autoencoder Machine Learning models. It determines trajectory outliers and quantifies a level of abnormality, therefore giving hints about the nature of the detected situations. Results obtained on three different scenarios, with Mode S flight data collected over one year, show that this method is well suited to efficiently detect anomalous situations, ranging from classic air traffic controllers orders to more significant deviations. Detecting such situations is not only a necessary milestone for the generation of ATC orders in a realistic environment; this methodology could also be useful in safety studies for anomaly detection and estimation of probabilities of rare events; and in complexity and performance analyses for detecting actions in neighbouring sectors or estimating ATC workload

Iterative Learning of Speech Recognition Models for Air Traffic Control

utomatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in operational efficiency. Air Traffic Control (ATC) systems in operation rooms around the world generate large amounts of untranscribed speech and radar data each day, which can be utilized to build and improve ASR models. In this paper, we propose an iterative approach that utilizes increasing amounts of untranscribed data to incrementally build the necessary ASR models for an ATC operational area. Our approach uses a semi-supervised learning framework to combine speech and radar data to iteratively update the acoustic model, language model and command prediction model (i.e. prediction of possible commands from radar data for a given air traffic situation) of an ASR system. Starting with seed models built with a limited amount of manually transcribed data, we simulate an operational scenario to adapt and improve the models through semi-supervised learning. Experiments on two independent ATC areas (Vienna and Prague) demonstrate the utility of our proposed methodology that can scale to operational environments with minimal manual effort for learning and adaptation

Iterative Learning of Speech Recognition Models for Air Traffic Control

Automatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in operational efficiency. Air Traffic Control (ATC) systems in operation rooms around the world generate large amounts of untranscribed speech and radar data each day, which can be utilized to build and improve ASR models. In this paper, we propose an iterative approach that utilizes increasing amounts of untranscribed data to incrementally build the necessary ASR models for an ATC operational area. Our approach uses a semi-supervised learning framework to combine speech and radar data to iteratively update the acoustic model, language model and command prediction model (i.e. prediction of possible commands from radar data for a given air traffic situation) of an ASR system. Starting with seed models built with a limited amount of manually transcribed data, we simulate an operational scenario to adapt and improve the models through semi-supervised learning. Experiments on two independent ATC areas (Vienna and Prague) demonstrate the utility of our proposed methodology that can scale to operational environments with minimal manual effort for learning and adaptation