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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References
Alam, S., Shafi, K., Abbass, H.A., Barlow, M.: An ensemble approach for conflict detection in free flight by data mining. Transp. Res. Part C Emerg. Technol. 17(3), 298–317 (2009)
Alonso-Ayuso, A., Escudero, L.F., Olaso, P., Pizarro, C.: Conflict avoidance: 0–1 linear models for conflict detection & resolution. TOP 21(3), 485–504 (2013)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)
Bilimoria, K.: A geometric optimization approach to aircraft conflict resolution. In: 18th Applied Aerodynamics Conference, p. 4265 (2000)
Brittain, M., Wei, P.: Autonomous aircraft sequencing and separation with hierarchical deep reinforcement learning. In: Proceedings of the International Conference for Research in Air Transportation (2018)
Brittain, M.W., Wei, P.: One to any: distributed conflict resolution with deep multi-agent reinforcement learning and long short-term memory. In: AIAA Scitech 2021 Forum, p. 1952 (2021)
Erzberger, H., Paielli, R.A., Isaacson, D.R., Eshow, M.M.: Conflict detection and resolution in the presence of prediction error. In: 1st USA/Europe Air Traffic Management R&D Seminar, Saclay, France, pp. 17–20. Citeseer (1997)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jiang, X.R., Wen, X.X., Wu, M.G., Wang, Z.K., Qiu, X.: A SVM approach of aircraft conflict detection in free flight. J. Adv. Transp. 2018(4), 1–9 (2018)
Kim, K., Hwang, I., Yang, B.J.: Classification of conflict resolution methods using data-mining techniques. In: 16th AIAA Aviation Technology, Integration, and Operations Conference, p. 4075 (2016)
Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, vol. 14, Montreal, Canada, pp. 1137–1145 (1995)
Kuchar, J.K., Yang, L.C.: A review of conflict detection and resolution modeling methods. IEEE Trans. Intell. Transp. Syst. 1(4), 179–189 (2000)
Lapasset, L., Rahman, M.S., Mothe, J.: Solving aircraft conflicts: data resources. In: 1st International Conference on Cognitive Aircraft Systems (ICCAS 2020), p. 76 (2020)
Pham, D.T., Tran, N.P., Alam, S., Duong, V., Delahaye, D.: A machine learning approach for conflict resolution in dense traffic scenarios with uncertainties (2019)
Pham, D.T., Trant, N.P., Goh, S.K., Alam, S., Duong, V.: Reinforcement learning for two-aircraft conflict resolution in the presence of uncertainty. In: 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF), pp. 1–6. IEEE (2019)
Prandini, M., Lygeros, J., Nilim, A., Sastry, S.: A probabilistic framework for aircraft conflict detection. In: Guidance, Navigation, and Control Conference and Exhibit, p. 4144 (1999)
Rahman, M.S.: Supervised machine learning model to help controllers solving aircraft conflicts. In: Bellatreche, L., et al. (eds.) TPDL/ADBIS -2020. CCIS, vol. 1260, pp. 355–361. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55814-7_31
Rahman, M.S., Lapasset, L., Mothe, J.: Multi-label classification of aircraft heading changes using neural network to resolve conflicts. arXiv preprint arXiv:2109.04767 (2021)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Srinivasamurthy, A., et al.: Iterative learning of speech recognition models for air traffic control. In: INTERSPEECH, pp. 3519–3523 (2018)
Zhao, P., Liu, Y.: Physics informed deep reinforcement learning for aircraft conflict resolution. IEEE Trans. Intell. Transp. Syst., 1–14 (2021). https://ieeexplore.ieee.org/abstract/document/9430767
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Rahman, M.S., Lapasset, L., Mothe, J. (2022). Aircraft Conflict Resolution Using Convolutional Neural Network on Trajectory Image. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_75
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