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Aircraft Conflict Resolution Using Convolutional Neural Network on Trajectory Image

Abstract : A situation between several moving aircraft is a conflict when their position is less than the internationally specified distance. To solve aircraft conflicts, air traffic controllers consider many parameters including the positioning coordinate, speed, direction, weather, etc. of the involved aircraft. This is a complex task, specifically considering the increase of the traffic. Assisting systems could help controllers in their tasks. Most conflict resolution models are based on trajectory data of a fixed number of input aircraft. Under this constraint, it is possible to resolve conflicts using machine learning models, including convolutional neuron network models. Such models cannot resolve conflicts that imply a variable number of aircraft because the input size of the model is fixed. To solve this challenge, we transformed the trajectory data into images which size does not depend on the number of planes. We developed a multi-label conflict resolution model that we named ACRnet, based on a convolutional neural network to classify the obtained images. ACRnet model achieves an accuracy of 99.16% on the training data and of 98.97% on the test data set for two aircraft. For both two and three aircraft, the accuracy is 99.05% (resp. 98.96%) on the training (resp. test) data set.
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https://hal.archives-ouvertes.fr/hal-03628435
Contributor : Md Siddiqur Rahman Connect in order to contact the contributor
Submitted on : Monday, April 4, 2022 - 8:01:48 AM
Last modification on : Monday, July 4, 2022 - 9:34:24 AM
Long-term archiving on: : Tuesday, July 5, 2022 - 6:10:47 PM

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Md Siddiqur Rahman, Laurent Lapasset, Josiane Mothe. Aircraft Conflict Resolution Using Convolutional Neural Network on Trajectory Image. Ajith Abraham; Niketa Gandhi; Thomas Hanne; Tzung-Pei Hong; Tatiane Nogueira Rios; Weiping Ding. Intelligent Systems Design and Applications. 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021) Held During December 13–15, 2021, 418, Springer, pp.806-815, 2022, Lecture Notes in Networks and Systems, 978-3-030-96307-1. ⟨10.1007/978-3-030-96308-8_75⟩. ⟨hal-03628435⟩

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