OBZOR METODOV PREDUPREZhDENIYa KONFLIKTOV PRI UPRAVLENII VOZDUShNYM DVIZhENIEM S POMOShch'Yu GLUBOKOGO OBUChENIYa S PODKREPLENIEM

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Acesso é pago ou somente para assinantes

Resumo

Представлен обзор развития современных подходов к предупреждению конфликтов между воздушными судами на основе глубокого обучения с подкреплением. Рассмотрена базовая концепция обучения с подкреплением и некоторые основные алгоритмы, используемые для предупреждения конфликтов воздушных судов. Представлены модели с дискретными и непрерывными действиями по предупреждению конфликтов в двумерном и трехмерном воздушном пространстве при движении по фиксированным траекториям или в свободном полете. Рассмотрены различные подходы к представлению информации о состоянии воздушного пространства (с помощью вектора состояния и в виде графа) и разные типы взаимодействия между воздушными судами (на основе информации о состоянии окружающих воздушных судов или при помощи обмена сообщениями).

Sobre autores

E. KULIDA

Институт проблем управления им. В.А. Трапезникова РАН, Москва

Email: elena-kulida@yandex.ru
канд. техн. наук Москва, Россия

V. LEBEDEV

Институт проблем управления им. В.А. Трапезникова РАН, Москва

Email: lebedev-valentin@yandex.ru
д-р техн. наук Москва, Россия

Bibliografia

  1. International Civil Aviation Association. Doc 4444: Air Traffic Management // Procedures for Air Navigation Services, 16th ed. ICAO: Montreal, QC, Canada. 2016.
  2. Kulida E.L., Lebedev V.G. Methods for Solving Some Problems of Air Traffic Planning and Regulation. PART I: Strategic Planning of 4D Trajectories // Control Sciences. 2023. No. 1. P. 2–11. https://doi.org/10.25728/cs.2023.1.1
  3. Brittain M., Wei P. Autonomous Separation Assurance in an High-Density En Route Sector: a Deep Multi-Agent Reinforcement Learning Approach // The 22nd IEEE Intelligent Transportation Systems Conference (ITSC). Auckland, New Zealand, 2019. https://doi.org/10.109/ITSC.2019.8917217
  4. Пономарев К.Ю. Метод оценки динамической воздушной обстановки на конфликтность посредством полихромного отображения объектов в информационном обеспечении диспетчера управления воздушным движением // Автореф. дис. канд. техн. наук. СПб.: ФГБОУ ВО СПбГУ ГА им. А.А. Новикова, 2023. 24 с.
  5. Erzberger H. Automated Conflict Resolution for Air Traffic Control // 25th International Congress of the Aeronautical Sciences (ICAS). Germany, Hamburg. 2006.
  6. Farley T., Field M., Erzberger H. Fast-time Simulation Evaluation of a Conflict Resolution Algorithm Under High Air Traffic Demand. 2007. https://www.researchgate.net/publication/255062615
  7. Erzberger H., Heere K. 2010. Algorithm and Operational Concept for Resolving Short-range Conflicts // Proc. Inst. Mechan. Engin., Part G: J. Aerospac. Engin. 2010. V. 224. No. 2. P. 225–243. https://doi.org/10.1243/09544100JAERO546
  8. Hoekstra J.M., van Gent R.N.H.W., Ruigrok R.C.J. Designing for Safety: The ‘free flight’ Air Traffic Management Concept // Reliab. Engin. Syst. Safet. 2002. V. 75. No. 2. P. 215–232. https://doi.org/10.1016/S0951-8320(01)00096-5
  9. Clari M.V., Ruigrok R.C.J., Hoekstra J.M., Visser H.G. Cost-benefit Study of Free Flight with Airborne Separation Assurance // Air Traffic Control Quarterly. 2001. V. 9. No. 4. P. 287–309. https://doi.org/10.2514/atcq.9.4.287
  10. Марьин Н.П. Перспектива внедрения концепции «свободный полет» // Проблемы безопасности полетов. 2009. № 5. С. 42–55.
  11. Yang Y., Zhang J., Cai K., Prandini M. Multi-aircraft Conflict Detection and Resolution Based on Probabilistic Reach Sets // IEEE Transactions on Control Systems Technology. 2016. V. 25. No. 1. P. 309–316. https://doi.org/10.1109/TCST.2016.2542046
  12. Орлов В.С. Разработка и исследование алгоритмов обнаружения и предотвращения опасных сближений в воздухе в рамках перспективной системы ОрВД // Дисс.. . . канд. техн. наук. М. ФГБОУ В ПО «Московский авиационный институт (национальный исследовательский университет)». 2015. 116 с.
  13. Буркин В.С. Синтез алгоритмов обнаружения и разрешения конфликтовстолкновений воздушных судов по информации системы автоматического зависимого наблюдения в условиях неопределенности // Изв. РАН. Теория и системы управления. 2017. № 3. С. 157–169. https://doi.org/10.7868/S0002338817030088
  14. Кумков С.И., Пятко С.Г. Быстрые алгоритмы обнаружения конфликтных ситуаций между воздушными судами / «Теория оптимального управления и приложения (ОСТА 2022)». Материалы международной конференции. Ин-т мат. и механики им. Н.Н. Красовского (ИММ УрО РАН). Екатеринбург. 2022. С. 126–131.
  15. Pelegrin M., D’Ambrosio C. Aircraft Deconfliction via Mathematical Programming: Review and Insights // Transportation Science. 2022. V. 56. No. 1. P. 118–140. https://doi.org/10.1287/trsc.2021.1056
  16. Cafieri S., Conn A.R., Mongeau M. Mixed-integer Nonlinear and Continuous Optimization Formulations for Aircraft Conflict Avoidance via Heading and Speed Deviations // Eur. J. Oper. Res. 2023. V. 310. No. 2. P. 670–679. https://doi.org/10.1016/j.ejor.2023.03.002
  17. Dias F., Rey D. Aircraft Conflict Resolution with Trajectory Recovery Using Mixedinteger Programming // J. Global Optim. 2024. V. 90. P. 1031–1067. https://doi.org/10.1007/s10898-024-01393-1
  18. Cecen R.K., Cetek C. Conflict-free En-route Operations with Horizontal Resolution Manoeuvers Using a Heuristic Algorithm // Aeronaut. J. 2020. V. 124. P. 767–785. https://doi.org/10.1017/aer.2020.5
  19. Eby M. A Self-organizational Approach for Resolving Air Traffic Conflicts // Lincoln Lab. J. 1994. V. 7. No. 2. P. 239–254.
  20. Balasooriyan S. Multi-aircraft Conflict Resolution Using Velocity Obstacles // MSc thesis. Delft University of Technology. 2017. 126 с.
  21. Durand N. Constant Speed Optimal Reciprocal Collision Avoidance // Transportation Research. Part C, Emerging Technologies. 2018. P. 366–379. https://doi.org/0.1016/j.trc.2018.10.004
  22. Pan W., Qin L., He Q., Huang Y. Three-Dimensional Flight Conflict Detection and Resolution Based on Particle Swarm Optimization // Aerospace. 2023. V. 10. No. 9. https://doi.org/10.3390/aerospace10090740
  23. Sui D., Zhang K. A Tactical Conflict Detection and Resolution Method for En Route Conflicts in Trajectory-Based Operations // J. Advanc. Transport. 2022. No. 2. P. 1–16. https://doi.org/10.1155/2022/9283143
  24. Brittain M., Wei P. Scalable Autonomous Separation Assurance with Heterogeneous Multi-agent Reinforcement Learning // IEEE Transactions on Automation Science and Engineering. 2022. V. 19. No. 4. P. 2837–2848. https://doi.org/10.1109/TASE.2022.3151607
  25. Самойлов В.А., Доенко Д.В. Возможность применения нейронных сетей для поиска и решения потенциальных конфликтных ситуаций между воздушными судами при полетах в верхнем воздушном пространстве / Транспорт России: проблемы и перспективы – 2022. Материалы Международной научно-практической конференции. Институт проблем транспорта им. Н.С. Соломенко РАН. СанктПетербург. 2022. С. 180–184.
  26. Wang Z., Liang M., Delahaye D. Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning // 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT). Singapore. 2020. https://doi.org/10.1109/AIDA-AT48540.2020.904.9180
  27. Pinto Neto E.C., Baum D., Almeida J.R., et. al. Deep Learning in Air Traffic Management (ATM): Applications, Opportunities, and Open Challenges // Aerospace 2023. V. 10. No. 4. https://doi.org/10.3390/aerospace10040358
  28. Razzaghi P., Tabrizian A., Guo W., et. al. A Survey on Reinforcement Learning in Aviation Applications // Engineering Applications of Artificial Intelligence. 2024. V. 136. No. 3. https://doi.org/10.1016/j.engappai.2024.108911
  29. Kulida E.L., Lebedev V.G. Methods for Solving Some Problems of Air Traffic Planning and Regulation. PART II: Application of Deep Reinforcement Learning // Control Sciences. 2023. No. 2. P. 2–14. https://doi.org/10.25728/cs.2023.2.2
  30. Brittain M.W., Wei P. One to Any: Distributed Conflict Resolution with Deep Multi-agent Reinforcement Learning and Long Short-term Memory // AIAA Scitech 2021 Forum. Nashville, Tennessee, USA. https://doi.org/10.2514/6.2021-1952
  31. Wang Z., Pan W., Li H., et. al. Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control // Aerospace. 2022. V. 9. No. 6. https://doi.org/10.3390/aerospace9060294
  32. Groot J., Ribeiro M., Ellerbroek J., et. al. Improving Safety of Vertical Manoeuvres in a Layered Airspace with Deep Reinforcement Learning // International Conference on Research in Air Transportation (ICRAT). Tampa, Florida, USA. 2022. P. 19–23.
  33. Ribeiro M., Ellerbroek J., Hoekstra J. Review of Conflict Resolution Methods for Manned and Unmanned Aviation // Aerospace. 2020. V. 7. No. 6. https://doi.org/10.3390/aerospace7060079
  34. Ribeiro M., Ellerbroek J., Hoekstra J. Distributed Conflict Resolution at High Traffic Densities with Reinforcement Learning // Aerospace. 2022. V. 9. No. 9. https://doi.org/10.3390/aerospace9090472
  35. Ribeiro M. Conflict Resolution at High Traffic Densities with Reinforcement Learning // Thesis for PhD. 2023. https://doi.org/10.4233/uuid:a2979919-cb01-41d1-bbba-fefa9079463b
  36. Ribeiro M., Ellerbroek J., Hoekstra J. Improving Algorithm Conflict Resolution Manoeuvres with Reinforcement Learning // Aerospace. 2022. V. 9. No. 12. https://doi.org/10.3390/aerospace9120847
  37. Визильтер Ю.В., Вишняков Б.В., Желтов С.Ю. Современные технологии искусственного интеллекта и их применение в авиационных комплексах // XVI Всероссийская мультиконференция по проблемам управления (МКПУ– 2023). Волгоград. Материалы мультиконференции в 4 т. Т. 3. С. 13–16.
  38. Sui D., Ma C., Wei C. Tactical Conflict Solver Assisting Air Traffic Controllers Using Deep Reinforcement Learning // Aerospace. 2023. V. 10. No. 2.https://doi.org/10.3390/aerospace10020182
  39. Bastas A., Vouros G. Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts // Aerospace. 2023. V. 10. No. 6.https://doi.org/10.3390/aerospace10060557
  40. Кулида Е.Л., Лебедев В.Г. Проблемы при применении методов машинного обучения в авиации // Труды 16-й Международной конференции «Управление развитием крупномасштабных систем» (MLSD’2023, Москва). М.: ИПУ РАН, 2023. С. 1315–1320. https://doi.org/10.25728/mlsd.2023.1315
  41. Degas A., Islam M.R., Hurter C., et. al. A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory // Appl. Sci. 2022. V. 12. Iss. 3.https://doi.org/10.3390/app12031295
  42. Wang L., Yang H., Lin Y., et. al. Enhancing Air Traffic Control: A Transparent Deep Reinforcement Learning Framework for Autonomous Conflict Resolution // Expert Systems with Applications. 2024. V. 260(2). https://doi.org/10.06/j.eswa.2024.125389
  43. Саттон Р.С., Барто Э.Г. Обучение с подкреплением. М.: ДМК-Пресс, 2020.
  44. Грессер Л., Кенг В.Л. Глубокое обучение с подкреплением: теория и практика на языке Python. СПб.: Питер, 2022.
  45. Моралес М. Грокаем глубокое обучение с подкреплением. СПб.: Питер, 2023.
  46. Sui D., Ma C., Dong J. Conflict Resolution Strategy Based on Deep Reinforcement Learning for Air Traffic Management // Aviation. 2023. V. 27. Iss. 3. P. 177–186. https://doi.org/10.3846/aviation.2023.19720
  47. Hasselt H.V. Double q-learning // 24th Annual Conference on Neural Information Processing Systems. Vancouver, Canada. 2010. P. 2613–2621.
  48. Brittain M., Wei P. Autonomous Aircraft Sequencing and Separation with Hierarchical Deep Reinforcement Learning // International Conference for Research in Air Transportation. Castelidefeil, Spain. 2018.
  49. Mollinga J., Hoof H. An Autonomous Free Airspace En-route Controller Using Deep Reinforcement Learning Techniques // 9th International Conference on Research in Air Transportation (ICRAT). Tampa, Florida, USA. 2020.
  50. Sui D., Xu W., Zhang K. Study on the Resolution of Multi-aircraft Flight Conflicts Based on an IDQN // Chin. J. Aeronaut. 2021. V. 35. No. 11. P. 195–213.https://doi.org/10.06/j.cja.2021.03.015
  51. Li S., Egorov M., Kochenderfer M.J. Optimizing Collision Avoidance in Dense Airspace Using Deep Reinforcement Learning // Proceedings of the 13th USA/Europe Air Traffic Management Research and Development Seminar. Vienna, Austria. 17–21 June 2019. https://doi.org/10.48550/arXiv.1912.10146
  52. Hermans M.C. Towards Explainable Automation for Air Traffic Control Using Deep Q-Learning from Demonstrations and Reward Decomposition. Master’s Thesis, Delft University of Technology, Delft, The Netherlands, May 2020.
  53. Pham D.-T., Tran N.P., Goh S.K., et. al. Reinforcement Learning for Two-aircraft Conflict Resolution in the Presence of Uncertainty // IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF). Singapore. 2019. https://doi.org/10.1109/RIVF.2019.8713624
  54. Wang Z., Li H., Wang J., et. al. Deep Reinforcement Learning Based Conflict Detection and Resolution in Air Traffic Control // IET Intelligent Transport Systems. 2019. V. 13. Iss. 6. P. 1041–1047. https://doi.org/10.1049/iet-its.2018.5357
  55. Badea C.A., Groot J., Morfin Veytia A., et. al. Lateral and Vertical Air Traffic Control Under Uncertainty Using Reinforcement Learning // Proceedings of the 12th SESAR Innovation Days. Budapest, Hungary. 2022.
  56. Pham D., Tran N., Alam S., et. al. A Machine Learning Approach for Conflict Resolution in Dense Traffic Scenarios with Uncertainties // 13th USA/Europe Air Traffic Management Research and Development Seminar. Vienne, Austria. Jun. 2019.
  57. Wen H., Li H., Wang Z. Application of DDPG-based Collision Avoidance Algorithm in Air Traffic Control // 12nd International Symposium on Computational Intelligence and Design. Hangzhou, China. 2019. P. 130–133.https://doi.org/10.1109/ISCID.2019.00036
  58. Pham D.-T., Tran P.N., Alam S., et. al. Deep Reinforcement Learning Based Path Stretch Vector Resolution in Dense Traffic with Uncertainties // Transportation Research, Part C. 2022. V. 135. No. 3. https://doi.org/10.1016/j.trc.2021.103463
  59. Mukherjee A., Guleria Y., Alam S. Deep Reinforcement Learning for Air Traffic Conflict Resolution Under Traffic Uncertainties // 41st Digital Avionics Systems Conference (DASC). Portsmouth, USA. 2022.https://doi.org/10.1109/DASC55683.2022.9925772
  60. Sunil E., Ellerbroek J., Hoekstra J.M. Camda: Capacity Assessment Method for Decentralized Air Traffic Control // International Conference on Research in Air Transportation (ICRAT). Barcelona, Spain. 2018. P. 26–29.
  61. Dudoit A., Rimsa V., Bogdevicius M. Investigation of Aircraft Conflict Resolution Trajectories under Uncertainties // Sensors. 2024. V. 24. No. 18.https://doi.org/10.3390/s24185877
  62. Busoniu L., Babuska R., De Schutter B. A Comprehensive Survey of Multi-agent Reinforcement Learning // IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2008. V. 38. No. 2. P. 156–172.https://doi.org/10.1109/TSMCC.2007.913919
  63. Tan M. Multi-agent Reinforcement Learning: Independent vs. Cooperative Agents // 10th International Conference on Machine Learning (ICML). 1993. P. 330–337. https://doi.org/10.1016/B978-1-55860-307-3.50049-6
  64. Matignon L., Laurent G.J., Le Fort-Piat N. Independent Reinforcement Learners in Cooperative Markov Games: a Survey Regarding Coordination Problems // Knowledge Engineering Review. 2012. V. 27. No. 1.https://doi.org/10.1017/S0269888912000057
  65. Everett M., Chen Y.F., How J.P. Motion Planning Among Dynamic, Decisionmaking Agents with Deep Reinforcement Learning // IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018. P. 3052–3059.https://doi.org/10.48550/arXiv.1805.01956
  66. Chen. Y., Hu M., Yang L., et. al. General Multi-agent Reinforcement Learning Integrating Adaptive Manoeuvre Strategy for Real-time Multi-aircraft Conflict Resolution // Transportation Research. Part C. Emerging Technologies. 2023. V. 151. https://doi.org/10.1016/j.trc.2023.104125
  67. Xu Q., Chen Z., Li F., et. al. An Efficient Aircraft Conflict Detection and Resolution Method Based on an Improved Reinforcement Learning Framework // Int. J. Aerospac. Engin. 2023. V. 1. P. 1–16. https://doi.org/10.1155/2023/6643903
  68. Ghosh S., Laguna S., Lim S.H., et. al. A Deep Ensemble Method for Multi-agent Reinforcement Learning: A Case Study on Air Traffic Control // 31st International Conference on Automated Planning and Scheduling. SuiGuangzhou, China. 2021. P. 468–476. https://doi.org/10.1609/icaps.v31i1.15993
  69. Chen Y., Xu Y., Yang L., et. al. General real-time three-dimensional multi-aircraft conflict resolution method using multi-agent reinforcement learning // Transportation Research. Part C. Emerging Technologies. V. 157.https://doi.org/10.1016/j.trc.2023.104367
  70. Hessel M., Modayil J., Van Hasselt H., et. al. Rainbow: Combining Improvements in Deep Reinforcement Learning // Thirty-Second AAAI Conference on Artificial Intelligence. 2018. V. 32. No. 3. https://doi.org/10.1609/aaai.v32i1.11796
  71. Nilsson J., Unger J., Eilertsen G. Self-Prioritizing Multi-Agent Reinforcement Learning for Conflict Resolution in Air Traffic Control with Limited Instructions // Aerospace. 2025. V. 12. No. 2. https://doi.org/10.3390/aerospace12020088
  72. Sui D., Zhou Z., Cui X. Priority-based Intelligent Resolution Method of Multiaircraft Flight Conflicts // Aeronaut. J. 2024. V. 129. P. 326–350.https://doi.org/10.1017/aer.2024.75
  73. Zhao P., Liu Y. Physics Informed Deep Reinforcement Learning for Aircraft Conflict Resolution // IEEE Transactions on Intelligent Transportation Systems. 2021. V. 23. No. 7. P. 8288–8301.
  74. Hochreiter S., Schmidhuber J. Long Short-Term Memory // Neural Computation. 1997. V. 9. No. 8. P. 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  75. Cho K., Merrienboer B., Gulcehre C., et. al. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation // Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar. 2014. P. 1724–1734. https://doi.org/10.3115/v1/D14-1179
  76. Brittain M.W., Yang X., Wei P. Autonomous Separation Assurance with Deep Multi-agent Reinforcement Learning // J. Aerospac. Inform. Syst. 2021. V. 18. No. 12. P. 890–905. https://doi.org/10.254/1.1010973
  77. Hoekstra J.M., Ellerbroek J. BlueSky ATC Simulator Project: an Open Data and Open Source Approach // 7th International Conference on Research in Air Transportation. Philadelphia, USA. 2016. V. 131. P. 132.
  78. Schulman J., Wolski F., Dhariwal P., et. al. Proximal Policy Optimization Algorithms // ArXiv. 2017. https://doi.org/10.48550/arXiv.1707.06347
  79. Vaswani A., Shazeer N., Parmar N., et. al. Attention is All You Need // 31st Conference on Neural Information Processing Systems (NIPS). Long Beach, USA. 2017. https://doi.org/10.48550/arXiv.1706.03762
  80. Dalmau R., Allard E. Air Traffic Control Using Message Passing Neural Networks and Multi-agent Reinforcement Learning // Conference: SESAR Innovation Days (SID), Virtual Event. 2020. P. 7–10.
  81. Groot J., Ellerbroek J., Hoekstra J. Using Relative State Transformer Models for Multi-Agent Reinforcement Learning in Air Traffic Control // Conference: SESAR Innovation days (SID). Seville, Spain. 2023.
  82. Wollkind S., Valasek J., Ioerger T. Automated Conflict Resolution for Air Traffic Management Using Cooperative Multi-agent Negotiation // AIAA Guidance, Navigation, and Control Conference and Exhibit. 2004.https://doi.org/10.2514/6.2004-4992
  83. Pritchett R., Genton A. Negotiated Decentralized Aircraft Conflict Resolution // IEEE Transactions on Intelligent Transportation Systems. 2017. V. 19. No. 1. P. 81–91. https://doi.org/10.1109/TITS.2017.2693820
  84. Lai J., Cai K., Liu Z., et. al. A Multi-agent Reinforcement Learning Approach for Conflict Resolution in Dense Traffic Scenarios // IEEE/AIAA 40th Digital Avionics Systems Conference (DASC). San Antonio, USA. 2021.https://doi.org/10.1109/DASC52595.2021.9594437
  85. Wu Z., Pan S., Chen F., et. al. A comprehensive Survey on Graph Neural Networks // 2019. https://doi.org/10.48550/arXiv.1901.00596
  86. Mendonca M., Ziviani A., Barreto A. Graph-Based Skill Acquisition for Reinforcement Learning // ACM Computing Surveys (CSUR). 2019. V. 52. No. 1.https://doi.org/10.1145/3291045
  87. Papadopoulos G., Bastas A., Vouros G.A., et. al. Deep Reinforcement Learning in Service of Air Traffic Controllers to Resolve Tactical Conflicts // Expert Systems with Applications. 2024. V. 236. No. 1. https://doi.org/10.1016/j.eswa.2023.121234
  88. Isufaj R., Aranega Sebastia D., Piera M.A. Toward Conflict Resolution with Deep Multi-agent Reinforcement Learning // J. Air Transport. 2022. V. 30. No. 3. P. 71–80. https://doi.org/10.2514/1.DO296
  89. Vouros G., Papadopoulos G., Bastas A., et. al. Automating the Resolution of Flight Conflicts: Deep Reinforcement Learning in Service of Air Traffic Controllers // PAIS 2022. P. 72–85. https://doi.org/10.48550/arXiv.2206.07403
  90. Kipf T.N., Welling M. Semi-supervised Classification with Graph Convolutional Networks // 2017. https://doi.org/10.48550/arXiv.1609.02907
  91. Velickovic P., Cucurull G., Casanova A., et. al. Graph Attention Networks // 2018. https://doi.org/10.48.550/arXiv.170.0903
  92. Zhang Y., Xu S., Zhang L., et. al. Short-term Multi-step-ahead Sector-Based Traffic Flow Prediction Based on the Attention-enhanced Graph Convolutional LSTM Network (AGC-LSTM) // Neural Computing and Applications. 2024. https://doi.org/10.1007/s00521-024-09827-3
  93. Li Y., Zhang Y., Guo T., et. al. Graph Reinforcement Learning for Multi-Aircraft Conflict Resolution // IEEE Transactions on Intelligent Vehicles. 2024. https://doi.org/10.1109/TIV.2024.3364652
  94. Ribeiro M., Tseremoglou I., Santos B. Certification of Reinforcement Learning Applications for Air Transport Operations Based on Criticality and Autonomy // AIAA Science and Technology Forum and Exposition. Orlando, Florida, USA. 2024. https://doi.org/10.2514/6.2024-1463

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML

Declaração de direitos autorais © Russian Academy of Sciences, 2025