Algorithms for Railway Embedded Control Devices for Safety Manoeuvres
DOI:
https://doi.org/10.2478/ecce-2020-0014Keywords:
Convolutional Neural Network, Electric transport, Locomotive braking system, Microcontroller, Object recognition, Railway, Safety, Traffic lightAbstract
This study is dedicated to solve manoeuvres making task while working on the station with no marshalling hump. It is part of the project aimed at the development of intelligent safety and optimal control systems of autonomous electric vehicles and transport in general. The main manoeuvres safety depends on the lack of items and other objects on the rails as well as on the position of turnouts. In most cases rails, occupied with other wagons, as well as the wrong position of turnouts are marked with prohibiting red or blue signals of the traffic light. The authors propose an algorithm for the traffic light recognition by using a convolutional neural network (CNN) and traffic light indicator recognition. However, the situation when the locomotive needs to drive on the rails occupied with other wagons, for example, during the manoeuvres on the railway station can also appear. For this purpose, the authors have developed a CNN algorithm for the wagon recognition on the rails.References
G. Stana, V. Brazis, and P. Apse-Apsitis, “Virtual Energy Simulation of Induction Traction Drive Test Bench,” Proceedings IEEE 2nd Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), Lithuania, pp. 75–80, 2014. https://doi.org/10.1109/AIEEE.2014.7020330
G. Stana, V. Brazis, “Modeling of two-trolleybus motion with braking energy exchange and transmission resistance,” Proceedings of 25th International Workshop on Electric Drives: Optimization in Control of Electric Drives (IWED), Moscow, Russia, 2018. https://doi.org/10.1109/IWED.2018.8321401
A. Nikolajevs, M. Mezitis, “Level Crossing Time Prediction,” Proceedings of 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Latvia, pp. 199–202, 2016. https://doi.org/10.1109/RTUCON.2016.7763105
European Railway Agency, “Railway Safety Performance in the European Union”, 2010, 64 p.
B. M. Smitienko, World economy. Moscow, Russia: Izdatel’stvo Yurayt, 2013, 590 p.
Railway Gazette, “UK’s first ETCS Level 2 signalling goes live”, 2010. [Online]. Retrieved from: https://www.railwaygazette.com/news/infrastructure/single-view/view/uks-first-etcs-level-2-signalling-goes-live.html
T. Yuan, Y. Chen, S. Tuo, and Z. Xiaoqing, “Research on the identification and classification of human error factors of metro operation without ATP,” proceedings of 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE), Singapore, 2017. https://doi.org/10.1109/ICITE.2017.8056926
J. Vijay, K. Yoneda, Z. Liu and S. Mita, “Saliency Map Generation by the Convolutional Neural Network for Real-Time Traffic Light Detection Using Template Matching,” IEEE Transactions on Computational Imaging, vol. 1, no. 3, pp. 159–173, September 2015. https://doi.org/10.1109/TCI.2015.2480006
S-H. Lee, J-H. Kim, Y-J. Lim and J. Lim, “Traffic light detection and recognition based on Haar-like features,” 2018 International Conference on Electronics, Information, and Communication (ICEIC), IEEE, January 2018. https://doi.org/10.23919/ELINFOCOM.2018.8330598
P. Zeller, B. Siebler, A. Lehner, and S. Sand, “Relative train localization for cooperative maneuvers using GNSS pseudoranges and geometric track information” proceedings of International Conference on Location and GNSS (ICL-GNSS), Gothenburg, Sweden, 2015. https://doi.org/10.1109/ICL-GNSS.2015.7217165
A. Potapovs, “Assessment of adaptive algorithms for automatic control of train braking system”, Ph.D. thesis, Riga Technical University, 2014.
A. Potapovs, A. Ļevčenkovs, M. Gorobecs, S. Holodovs, and I. Birjulins, “Train’s smooth and precise braking device,” LV Patent 14917B, 20 October 2014, Riga Technical University.
M. Mazumdar, V. Sarasvathi, and A. Kumar, “Object recognition in videos by sequential frame extraction using convolutional neural networks and fully connected neural networks,” proceedings of 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2018. https://doi.org/10.1109/ICECDS.2017.8389692
S. Birtane, E. Canayaz, Z. A. Altikardes, and H. Korkmaz, “Development of decision support system using Mamdani type fuzzy logic clusters for metabolic syndrome risk assesment,” proceedings of 2017 Medical Technologies National Congress (TIPTEKNO), Trabzon, Turkey, 2017. https://doi.org/10.1109/TIPTEKNO.2017.8238035
A. Beinarovica, M. Gorobetz, and A. Levchenkovs, “Neural Network in Turn Recognition Tasks for Electric Transport Safety,” Proceedings of 58th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON2017), Riga, 2017. https://doi.org/10.1109/RTUCON.2017.8124785
G. Stana, V. Brazis, “Modeling of two-trolleybus motion with braking energy exchange and transmission resistance,” Proceedings of 25th International Workshop on Electric Drives: Optimization in Control of Electric Drives (IWED), Moscow, Russia, 2018. https://doi.org/10.1109/IWED.2018.8321401
A. Nikolajevs, M. Mezitis, “Level Crossing Time Prediction,” Proceedings of 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Latvia, pp. 199–202, 2016. https://doi.org/10.1109/RTUCON.2016.7763105
European Railway Agency, “Railway Safety Performance in the European Union”, 2010, 64 p.
B. M. Smitienko, World economy. Moscow, Russia: Izdatel’stvo Yurayt, 2013, 590 p.
Railway Gazette, “UK’s first ETCS Level 2 signalling goes live”, 2010. [Online]. Retrieved from: https://www.railwaygazette.com/news/infrastructure/single-view/view/uks-first-etcs-level-2-signalling-goes-live.html
T. Yuan, Y. Chen, S. Tuo, and Z. Xiaoqing, “Research on the identification and classification of human error factors of metro operation without ATP,” proceedings of 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE), Singapore, 2017. https://doi.org/10.1109/ICITE.2017.8056926
J. Vijay, K. Yoneda, Z. Liu and S. Mita, “Saliency Map Generation by the Convolutional Neural Network for Real-Time Traffic Light Detection Using Template Matching,” IEEE Transactions on Computational Imaging, vol. 1, no. 3, pp. 159–173, September 2015. https://doi.org/10.1109/TCI.2015.2480006
S-H. Lee, J-H. Kim, Y-J. Lim and J. Lim, “Traffic light detection and recognition based on Haar-like features,” 2018 International Conference on Electronics, Information, and Communication (ICEIC), IEEE, January 2018. https://doi.org/10.23919/ELINFOCOM.2018.8330598
P. Zeller, B. Siebler, A. Lehner, and S. Sand, “Relative train localization for cooperative maneuvers using GNSS pseudoranges and geometric track information” proceedings of International Conference on Location and GNSS (ICL-GNSS), Gothenburg, Sweden, 2015. https://doi.org/10.1109/ICL-GNSS.2015.7217165
A. Potapovs, “Assessment of adaptive algorithms for automatic control of train braking system”, Ph.D. thesis, Riga Technical University, 2014.
A. Potapovs, A. Ļevčenkovs, M. Gorobecs, S. Holodovs, and I. Birjulins, “Train’s smooth and precise braking device,” LV Patent 14917B, 20 October 2014, Riga Technical University.
M. Mazumdar, V. Sarasvathi, and A. Kumar, “Object recognition in videos by sequential frame extraction using convolutional neural networks and fully connected neural networks,” proceedings of 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2018. https://doi.org/10.1109/ICECDS.2017.8389692
S. Birtane, E. Canayaz, Z. A. Altikardes, and H. Korkmaz, “Development of decision support system using Mamdani type fuzzy logic clusters for metabolic syndrome risk assesment,” proceedings of 2017 Medical Technologies National Congress (TIPTEKNO), Trabzon, Turkey, 2017. https://doi.org/10.1109/TIPTEKNO.2017.8238035
A. Beinarovica, M. Gorobetz, and A. Levchenkovs, “Neural Network in Turn Recognition Tasks for Electric Transport Safety,” Proceedings of 58th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON2017), Riga, 2017. https://doi.org/10.1109/RTUCON.2017.8124785
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2020-12-01
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Copyright (c) 2020 Anna Beinaroviča et al., published by Sciendo
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Beinaroviča, A., Gorobetz, M., & Alps, I. (2020). Algorithms for Railway Embedded Control Devices for Safety Manoeuvres. Electrical, Control and Communication Engineering, 16(2), 95-101. https://doi.org/10.2478/ecce-2020-0014
