Roadmap and challenges for reinforcement learning control in railway virtual coupling
Tóm tắt
Từ khóa
Tài liệu tham khảo
Movingrail—moving block and virtual coupling new generations of rail signalling. https://cordis.europa.eu/project/id/826347.
X2rail3—advanced signalling, automation and communication system (ip2 and ip5)– prototyping the future by means of capacity increase, autonomy and flexible communication. https://projects.shift2rail.org/s2r_ip2_n.aspx?p=X2RAIL-3.
Di Meo C, Di Vaio M, Flammini F, Nardone R, Santini S, Vittorini V. Ertms/etcs virtual coupling: proof of concept and numerical analysis. IEEE Trans Intell Transp Syst. 2019;21(6):2545–56. https://elib.dlr.de/137137/1/X2R3-TD2.8%20Virtually%20Coupled%20Train%20Sets_pdf.pdf.
Singh P, Dulebenets MA, Pasha J, Gonzalez EDS, Lau Y-Y, Kampmann R. Deployment of autonomous trains in rail transportation: current trends and existing challenges. IEEE Access. 2021;9:91427–61.
Park J, Lee B-H, Eun Y. Virtual coupling of railway vehicles: gap reference for merge and separation, robust control, and position measurement. IEEE Trans Intell Transp Syst 2020
RAILS: Roadmaps for A.I. Integration in the Rail Sector (2021). https://rails-project.eu/
Schenker M. S2r innovation days presentation x2r3-td2.8 virtually coupled train sets (2020)
Flammini F, Marrone S, Nardone R, Petrillo A, Santini S, Vittorini V. Towards railway virtual coupling. In: 2018 IEEE international conference on electrical systems for aircraft, railway, ship propulsion and road vehicles & international transportation electrification conference (ESARS-ITEC). IEEE; 2018. p. 1–6
Nold M, Corman F. Dynamic train unit coupling and decoupling at cruising speed: systematic classification, operational potentials, and research agenda. J Rail Transp Plan Manag. 2021;18: 100241.
Quaglietta E. Analysis of platooning train operations under v2v communication-based signaling: Fundamental modelling and capacity impacts of virtual coupling. In: Proceedings of the 98th transportation research board annual meeting. Transportation Research Board (TRB); 2019
Quaglietta E, Wang M, Goverde RM. A multi-state train-following model for the analysis of virtual coupling railway operations. J Rail Transp Plan Manag. 2020;15: 100195.
Aoun J, Quaglietta E, Goverde RM. Investigating market potentials and operational scenarios of virtual coupling railway signaling. Transp Res Rec. 2020;2674(8):799–812.
Quaglietta E, Spartalis P, Wang M, Goverde RM, van Koningsbruggen P. Modelling and analysis of virtual coupling with dynamic safety margin considering risk factors in railway operations. J Rail Transp Plan Manag. 2022;22: 100313.
Matthias Grimm MP. Kupplungseinrichtung für Schienenfahrzeuge. https://patents.google.com/patent/DE102007050937A1/de
Wu Q, Ge X, Han Q-L, Wang B, Wu H, Cole C, Spiryagin M. Dynamics and control simulation of railway virtual coupling. Veh Syst Dyn 2022; 1–25. https://doi.org/10.1080/00423114.2022.2105241.
Zhang Y, Wang H. Topological manifold-based monitoring method for train-centric virtual coupling control systems. IET Intel Transp Syst. 2020;14(2):91–102.
Felez J, Kim Y, Borrelli F. A model predictive control approach for virtual coupling in railways. IEEE Trans Intell Transp Syst. 2019;20(7):2728–39.
Wu Z, Gao C, Tang T. A virtually coupled metro train platoon control approach based on model predictive control. IEEE Access. 2021;9:56354–63.
Liu Y, Liu R, Wei C, Xun J, Tang T. Distributed model predictive control strategy for constrained high-speed virtually coupled train set. IEEE Trans Veh Technol 2021;71(1):171–83.
Prathiba SB, Raja G, Dev K, Kumar N, Guizani M. A hybrid deep reinforcement learning for autonomous vehicles smart-platooning. IEEE Trans Veh Technol. 2021;70(12):13340–50.
Coppola A, Petrillo A, Rizzo R, Santini S. Adaptive cruise control for autonomous electric vehicles based on q-learning algorithm. In: 2021 AEIT international annual conference (AEIT). IEEE; 2021. p. 1–6
RAILS: Deliverable D2.1: WP2 Report on case studies and analysis of transferability from other sectors (safety and automation). 2021. https://rails-project.eu/downloads/deliverables
Ning L, Li Y, Zhou M, Song H, Dong H. A deep reinforcement learning approach to high-speed train timetable rescheduling under disturbances. In: 2019 IEEE intelligent transportation systems conference (ITSC). IEEE; 2019. p. 3469–3474
Shang M, Zhou Y, Fujita H. Deep reinforcement learning with reference system to handle constraints for energy-efficient train control. Inf Sci. 2021;570:708–21.
Schenker M, Parise R, Goikoetxea J. Concept and performance analysis of virtual coupling for railway vehicles. In: Proceedings of the 3rd SmartRaCon scientific seminar, vol. 38. Deutsches Zentrum für Luft-und Raumfahrt eV Institut für Verkehrssystemtechnik. 2021. p. 81–91
Yi S. Principles of railway location and design. Academic Press; 2017.
van Nunen E, Esposto F, Saberi AK, Paardekooper J-P. Evaluation of safety indicators for truck platooning. In: 2017 IEEE intelligent vehicles symposium (IV). IEEE; 2017. p. 1013–1018
Fiori C, Ahn K, Rakha HA. Power-based electric vehicle energy consumption model: model development and validation. Appl Energy. 2016;168:257–68.
Wu Y, Li SE, Cortés J, Poolla K. Distributed sliding mode control for nmiscar heterogeneous platoon systems with positive definite topologies. IEEE Trans Control Syst Technol. 2019;28(4):1272–83.
Rasmussen CE. Gaussian processes in machine learning. In: Summer school on machine learning. Springer; 2003. p. 63–71
Thakkar A, Lohiya R. A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges. Arch Comput Methods Eng. 2021;28(4):3211–43.
Lillicrap TP, Hunt JJ, Pritzel A, Heess N, Erez T, Tassa Y, Silver D, Wierstra D. Continuous control with deep reinforcement learning. 2015. arXiv preprint arXiv:1509.02971
Kiran BR, et al. Deep reinforcement learning for autonomous driving: a survey. IEEE Trans Intell Transp Syst. 2022;23(6):4909–26. https://doi.org/10.1109/TITS.2021.3054625.
RAILS: Deliverable D2.1: WP2 Report on case studies and analysis of transferability from other sectors (safety and automation). 2022. https://rails-project.eu/downloads/deliverables
Farag A, AbdelAziz OM, Hussein A, Shehata OM. Reinforcement learning based approach for multi-vehicle platooning problem with nmiscar dynamic behavior. https://www.researchgate.net/profile/Amr-Ramadan-6/publication/349313418_Reinforcement_Learning_Based_Approach_for_Multi-Vehicle_Platooning_Problem_with_Nonlinear_Dynamic_Behavior/links/602a65ec92851c4ed57317a3/Reinforcement-Learning-Based-Approach-for-Multi-Vehicle-Platooning-Problem-with-Nonlinear-Dynamic-Behavior.pdf.
Lin Y, McPhee J, Azad NL. Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Trans Intell Veh. 2020;6(2):221–31.
Liu Y, Zhou Y, Su S, Xun J, Tang T. An analytical optimal control approach for virtually coupled high-speed trains with local and string stability. Transp Res Part C Emerg Technol. 2021;125: 102886.
Zhu M, Wang Y, Pu Z, Hu J, Wang X, Ke R. Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving. Transp Res Part C Emerg Technol. 2020;117: 102662.
Aradi S. Survey of deep reinforcement learning for motion planning of autonomous vehicles. IEEE Trans Intell Transp Syst. 2022;23(2):740–59. https://doi.org/10.1109/TITS.2020.3024655.
Zhu Z, Lin K, Zhou J. Transfer learning in deep reinforcement learning: a survey. arXiv preprint arXiv:2009.07888 (2020)
Grigorescu S, Trasnea B, Cocias T, Macesanu G. A survey of deep learning techniques for autonomous driving. J Field Robot. 2020;37(3):362–86.