Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

Abstract

Given the ongoing interest in the application of Machine Learning (ML) techniques, the development of new Air Traffic Control (ATC) tools is paramount for the improvement of the management of the air transport system. This article develops an ATC tool based on ML techniques for conflict detection. The methodology develops a data-driven approach that predicts separation infringements between aircraft within airspace. The methodology exploits two different ML algorithms: classification and regression. Classification algorithms denote aircraft pairs as a Situation of Interest (SI), i.e., when two aircraft are predicted to cross with a separation lower than 10 Nautical Miles (NM) and 1000 feet. Regression algorithms predict the minimum separation expected between an aircraft pair. This data-driven approach extracts ADS-B trajectories from the OpenSky Network. In addition, the historical ADS-B trajectories work as 4D trajectory predictions to be used as inputs for the database. Conflict and SI are simulated by performing temporary modifications to ensure that the aircraft pierces into the airspace in the same time period. The methodology is applied to Switzerland’s airspace. The results show that the ML algorithms could perform conflict prediction with high-accuracy metrics: 99% for SI classification and 1.5 NM for RMSE.

Preliminary design of an ATC support tool for the implementation of the Ad Hoc Separation Minima concept in an en-route sector.

“…From the aircraft trajectories prediction, SI are analysed. A SI is a situation in which an aircraft pair is expected to intersect with a horizontal separation smaller than a predefined distance [10]. Usually, this predefined separation is specified by the ANSP, and is higher than the separation minima.…”

“…Several studies have delved into complex probabilistic models to grasp trajectory uncertainty [4], [5], [6], [7], [8]. Recently, in the context of Artificial Intelligence (AI), Machine Learning (ML) algorithms have also been employed for CD [9], [10].…”

Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor

“…One distinguishing feature of the CD tool is that it does not perform trajectory predictions and then analyses separation infringements, but it performs predictions for separation infringements based on historical data. This section is a summary of the work previously developed by the authors in [ 22 , 24 ]. Figure 1 shows a summary of the operational capability of the CD tool.…”

“…The novelty of this work does not fall in the CD model based on ML techniques because the concept has already been described in [ 22 ]. Therefore, the main contributions of this paper to the literature are as follows: This is one of the first learning assurance analyses performed for CD predictors in ATC.…”

Performance Impact Assessment of Reducing Separation Minima for En-Route Operations

“…Moving towards the Trajectory-Based (TB) separation, the probability of conflict was determined from data of the actual aircraft trajectory [23]. Finally, in the context of an Artificial Intelligence (AI) boom, Machine Learning (ML) algorithms are also being used for conflict detection [24].…”