Augmented Air Traffic Control System—Artificial Intelligence as Digital Assistance System to Predict Air Traffic Conflicts

Abstract

Today’s air traffic management (ATM) system evolves around the air traffic controllers and pilots. This human-centered design made air traffic remarkably safe in the past. However, with the increase in flights and the variety of aircraft using European airspace, it is reaching its limits. It poses significant problems such as congestion, deterioration of flight safety, greater costs, more delays, and higher emissions. Transforming the ATM into the “next generation” requires complex human-integrated systems that provide better abstraction of airspace and create situational awareness, as described in the literature for this problem. This paper makes the following contributions: (a) It outlines the complexity of the problem. (b) It introduces a digital assistance system to detect conflicts in air traffic by systematically analyzing aircraft surveillance data to provide air traffic controllers with better situational awareness. For this purpose, long short-term memory (LSTMs) networks, which are a popular version of recurrent neural networks (RNNs) are used to determine whether their temporal dynamic behavior is capable of reliably monitoring air traffic and classifying error patterns. (c) Large-scale, realistic air traffic models with several thousand flights containing air traffic conflicts are used to create a parameterized airspace abstraction to train several variations of LSTM networks. The applied networks are based on a 20–10–1 architecture while using leaky ReLU and sigmoid activation function. For the learning process, the binary cross-entropy loss function and the adaptive moment estimation (ADAM) optimizer are applied with different learning rates and batch sizes over ten epochs. (d) Numerical results and achievements by using LSTM networks to predict various weather events, cyberattacks, emergency situations and human factors are presented.

Innovative ideas for the use of augmented reality devices in aerodrome control towers

“…Other functionality exploiting the availability of different sensors (e.g. surveillance and video streams) or adding enhanced prediction capabilities on the basis of Artificial Intelligence [7] could be added to also guide controllers in carrying out operations using different kinds of visual and auditory cues with situation-adaptive information. Last but not least combinations of AR and automated system interaction and input technologies, such as speech recognition with Artificial Intelligence support, should be investigated further [8].…”

Artificial Intelligence in Aviation: New Professionals for New Technologies

“…The article [6] proposes using long short-term memory neural networks to analyze aircraft surveillance data to detect conflicts and provide air traffic controllers with improved situational awareness to transform air traffic management into a more automated, efficient, and safer next-generation system.…”

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

“…The research in [13] presents a digital system for assistance of pilot in prediction of air conflicts and it gives a platform for effective flight safety awareness through utilizing aircraft surveillance data. The dataset used in this study is large-scale which comprises of real time air traffic data including aircrafts conflicts data as well.…”