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Onboard Sensor Systems for Automatic Train Operation

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Dependable Computing – EDCC 2022 Workshops (EDCC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1656))

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Abstract

This paper introduces the specific requirements of the domain of train operation and its regulatory framework to the AI community. It assesses sensor sets for driverless and unattended train operation. It lists functionally justified ranges of technical specifications for sensors of different types, which will generate input for AI perception algorithms (i.e. for signal and obstacle detection). Since an optimal sensor set is the subject of research, this paper provides the specification of a generic data acquisition platform as a crucial step. Some particular results are recommendations for the minimal resolution and shutter type for image sensors, as well as beam steering methods and resolutions for LiDARs.

Supported by German Centre for Rail Traffic Research DISCLAIMER: This is not an official statement, guideline or directive of the German Federal Railway Authority.

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References

  1. Aeva Becomes First FMCW 4D LiDAR on NVIDIA DRIVE Autonomous Vehicle Platform. Businesswire (2022)

    Google Scholar 

  2. DB Fernverkehr AG: Richtlinie 418.10 - 90 “Triebfahrzeugführerheft der DB Fernverkehr AG”. DB Fernverkehr AG, Frankfurt, Version 10. Accessed 15 Dec 2019

    Google Scholar 

  3. DB Fernverkehr AG: Richtlinie 408.20 “Fahrdienstvorschrift - Richtlinien 408.21 - 27 und 408.48”. DB Netz AG, Frankfurt, Version 4.0. Accessed 12 Dec 2021

    Google Scholar 

  4. Baker, S., Bennett, E., Kang, S.B., Szeliski, R.: Removing Rolling Shutter Wobble. In: IEEE CVPR, pp. 2392–2399 (2010)

    Google Scholar 

  5. BMDV: Autonomes Fahren im Schienenverkehr. FE-Nr. 97.370/2016 (2018)

    Google Scholar 

  6. Bodensteiner, C., Bullinger, S., Arens, M.: Multispectral matching using conditional generative appearance modeling. In: IEEE AVSS, pp. 1–6 (2018)

    Google Scholar 

  7. Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv:1707.08819 (2017)

  8. Cordts, M., et al.: The Cityscapes dataset for semantic urban scene understanding. In: IEEE CVPR (2016)

    Google Scholar 

  9. DB Cargo AG, Mainz: DBCDE-003 “Regelbuch - Basisteil für Mitarbeiter im Bahnbetrieb (inkl. Führen von Triebfahrzeugen)”. DB Cargo AG, Mainz, Version 04. Accessed 15 Dec 2019

    Google Scholar 

  10. DB Regio AG: DBREGIO-003 “Regelbuch - Basisteil für Mitarbeiter im Bahnbetrieb (inkl. Führen von Triebfahrzeugen)”. DB Regio AG, Frankfurt, Version 04a. Accessed 13 Dec 2020

    Google Scholar 

  11. Deutsche Bahn AG: Rahmenrichtlinie 107.0000A02 Ärztliche Regeln: Med. Kriterien 5.0. In: Rahmenrichtlinie 107.0000 Grundlagen: Medizinische und psychologische Eignung 8.0 (2020)

    Google Scholar 

  12. Digitale Schiene Deutschland: Sensors4rail testet erstmals sensorbasierte Wahrnehmungssysteme im Bahnbetrieb (2021). https://digitale-schiene-deutschland.de/Sensors4Rail

  13. Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226–231 (1996)

    Google Scholar 

  14. European Commission: Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts (2021)

    Google Scholar 

  15. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: CVPR (2012)

    Google Scholar 

  16. Harb, J., Rébéna, N., Chosidow, R., Roblin, G., Potarusov, R., Hajri, H.: FRSign: a large-scale traffic light dataset for autonomous trains. arXiv:2002.05665 (2020)

  17. Hedborg, J., Forssén, P.E., Felsberg, M., Ringaby, E.: Rolling shutter bundle adjustment. In: IEEE CVPR, pp. 1434–1441 (2012)

    Google Scholar 

  18. Herrmann, C., Ruf, M., Beyerer, J.: CNN-based thermal infrared person detection by domain adaptation. In: Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything, vol. 10643, p. 1064308. SPIE (2018)

    Google Scholar 

  19. Ho, N., Pham, M., Vo, N.D., Nguyen, K.: Vehicle detection at night time. In: NAFOSTED NICS, pp. 250–255 (2020)

    Google Scholar 

  20. Kalb, T., Roschani, M., Ruf, M., Beyerer, J.: Continual learning for class-and domain-incremental semantic segmentation. In: 2021 IEEE IV, pp. 1345–1351 (2021)

    Google Scholar 

  21. Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report (2009)

    Google Scholar 

  22. Leinhos, D., et al.: Sensorik als technische Voraussetzung für ATO-Funktionen. Technical report Deutsches Zentrum für Schienenverkehrsforschung (2022)

    Google Scholar 

  23. Lin, C.T., Huang, S.W., Wu, Y.Y., Lai, S.H.: GAN-Based Day-to-Night Image Style Transfer for Nighttime Vehicle Detection. IEEE Trans. Intell. Transp. Syst. 22(2), 951–963 (2021)

    Article  Google Scholar 

  24. Liu, R., Yang, C., Sun, W., Wang, X., Li, H.: StereoGAN: bridging synthetic-to-real domain gap by joint optimization of domain translation and stereo matching. In: IEEE/CVF CVPR, pp. 12754–12763 (2020)

    Google Scholar 

  25. Pappaterra, M.J., Flammini, F., Vittorini, V., Bešinović, N.: A systematic review of artificial intelligence public datasets for railway applications. Infrastructures 6(10), 136 (2021)

    Article  Google Scholar 

  26. Ristić-Durrant, D., Franke, M., Michels, K.: A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways. Sensors 21(10), 3452 (2021)

    Article  Google Scholar 

  27. Rojtberg, P., Pöllabauer, T., Kuijper, A.: Style-transfer GANs for bridging the domain gap in synthetic pose estimator training. In: IEEE AIVR, pp. 188–195 (2020)

    Google Scholar 

  28. Sorokin, A., Forsyth, D.: Utility data annotation with Amazon Mechanical Turk. In: IEEE CVPR, pp. 1–8 (2008)

    Google Scholar 

  29. Tagiew, R., Buder, T., Hofmann, K., Klotz, C., Tilly, R.: Towards nucleation of GoA3+ approval process. In: HPCCT, pp. 41–47 (2021)

    Google Scholar 

  30. Tagiew, R., Buder, T., Tilly, R., Hofmann, K., Klotz, C.: Datensätze für das autonome Fahren als Grundlage für GoA3+. ETR - Eisenbahntechnische Rundschau 9 (2021). https://eurailpress-archiv.de/SingleView.aspx?show=2760103

  31. Triess, L.T., Dreissig, M., Rist, C.B., Zöllner, J.M.: A survey on deep domain adaptation for lidar perception. In: IEEE IV Workshops, pp. 350–357. IEEE (2021)

    Google Scholar 

  32. Wei, Y., Wei, Z., Rao, Y., Li, J., Zhou, J., Lu, J.: LiDAR distillation: bridging the beam-induced domain Gap for 3D object detection. arXiv:2203.14956 (2022)

  33. Yu, F., et al.: BDD100K: a diverse driving dataset for heterogeneous multitask learning. In: IEEE/CVF CVPR, pp. 2636–2645 (2020)

    Google Scholar 

  34. Zendel, O., Murschitz, M., Zeilinger, M., Steininger, D., Abbasi, S., Beleznai, C.: Railsem19: a dataset for semantic rail scene understanding. In: IEEE/CVF CVPR (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. German Centre for Rail Traffic Research at Federal Railway Authority, Dresden, Germany

    Rustam Tagiew & Christian Klotz

  2. DB Systemtechnik GmbH, Minden, Germany

    Dirk Leinhos, Henrik von der Haar & Pavel Klasek

  3. Fraunhofer IOSB, Karlsruhe, Germany

    Dennis Sprute & Jens Ziehn

  4. Technische Hochschule OWL, Lemgo, Germany

    Andreas Schmelter & Stefan Witte

Authors
  1. Rustam Tagiew
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  2. Dirk Leinhos
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  3. Henrik von der Haar
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  4. Christian Klotz
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  5. Dennis Sprute
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  6. Jens Ziehn
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  7. Andreas Schmelter
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  8. Stefan Witte
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  9. Pavel Klasek
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Corresponding author

Correspondence to Rustam Tagiew .

Editor information

Editors and Affiliations

  1. University of Campania "Luigi Vanvitelli", Caserta, Italy

    Stefano Marrone

  2. Gran Sasso Science Institute, L’Aquila, Italy

    Martina De Sanctis

  3. Budapest University of Technology and Economics, Budapest, Hungary

    Imre Kocsis

  4. Fraunhofer Institute for Experimental Software Engineering IESE, Kaiserslautern, Germany

    Rasmus Adler

  5. University of York, York, UK

    Richard Hawkins

  6. Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany

    Philipp Schleiß

  7. University of Naples Federico II, Naples, Italy

    Stefano Marrone

  8. Parthenope University of Naples, Naples, Italy

    Roberto Nardone

  9. Mälardalen University, Västerås, Sweden

    Francesco Flammini

  10. University of Naples Federico II, Naples, Italy

    Valeria Vittorini

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Tagiew, R. et al. (2022). Onboard Sensor Systems for Automatic Train Operation. In: Marrone, S., et al. Dependable Computing – EDCC 2022 Workshops. EDCC 2022. Communications in Computer and Information Science, vol 1656. Springer, Cham. https://doi.org/10.1007/978-3-031-16245-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-16245-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16244-2

  • Online ISBN: 978-3-031-16245-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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