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Test and Training Data Generation for Object Recognition in the Railway Domain

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Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops (SEFM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13765))

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Abstract

AI development and test is a data driven endeavor. To date it is magnitudes more laborious to collect and annotate training and test data, than to provide a problem matching architecture and train it. In the KI-LOK project, a case study seeks to validate an object recognition system to prevent potentially fatal behavior of autonomous train operations. To accommodate for the vast amount of possible scenarios a train could encounter during operation, we propose a tool chain to automatically generate labeled synthetic images and videos. We start from an ontology of elements such as: Tracks, houses, vehicles or signals, these elements are then sampled and modeled in 3D to represent a scenario. Since the objects and locations of the elements in a scenario are known, no manual annotation or labeling of the data is required. By sampling from an ontology it will be possible to build comprehensive and balanced datasets of scenarios to train and test AI, while adding the benefit of corner case generation by reducing to certain elements in the ontology. This article reports on the current status of the project and the goals it tries to achieve.

Supported by the German Federal Ministry for Economic Affairs and Climate Action.

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Notes

  1. 1.

    https://www.asam.net/project-detail/asam-openodd/.

  2. 2.

    https://www.asam.net/project-detail/asam-openxontology/.

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Acknowledgement

We would like to thank Daniel Gottlob for his help in preparing this work. The research project “KI-Lokomotivesysteme - Prüfverfahren für KI-basierte Komponenten im Eisenbahnbetrieb” (abbreviated KI-LOK, project website: https://ki-lok.itpower.de/) is funded by the German Federal Ministry for Economic Affairs and Climate Action and managed by TÜV Rheinland (project no.: 19121007A).

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Authors and Affiliations

  1. SQC - System Quality Center, Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS, Berlin, Germany

    Jürgen Grossmann, Sami Kharma, Dorian Knoblauch & Mariia Kucheiko

  2. ITPower Solutions GmbH, Berlin, Germany

    Nicolas Grube, Roman Krajewski & Hans-Werner Wiesbrock

Authors
  1. Jürgen Grossmann
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  2. Nicolas Grube
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  3. Sami Kharma
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  4. Dorian Knoblauch
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  5. Roman Krajewski
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  6. Mariia Kucheiko
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  7. Hans-Werner Wiesbrock
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Corresponding author

Correspondence to Hans-Werner Wiesbrock .

Editor information

Editors and Affiliations

  1. National Institute of Aerospace, Hampton, VA, USA

    Paolo Masci

  2. University of Pisa, Pisa, Italy

    Cinzia Bernardeschi

  3. University of Urbino, Urbino, Pesaro-Urbino, Italy

    Pierluigi Graziani

  4. Gesellschaft zur Förderung angewandter, Berlin, Germany

    Mario Koddenbrock

  5. University of Pisa, Pisa, Italy

    Maurizio Palmieri

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Grossmann, J. et al. (2023). Test and Training Data Generation for Object Recognition in the Railway Domain. In: Masci, P., Bernardeschi, C., Graziani, P., Koddenbrock, M., Palmieri, M. (eds) Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops. SEFM 2022. Lecture Notes in Computer Science, vol 13765. Springer, Cham. https://doi.org/10.1007/978-3-031-26236-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-26236-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26235-7

  • Online ISBN: 978-3-031-26236-4

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