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Topological Data Analysis in Automotive Industry

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23. Internationales Stuttgarter Symposium (ISSYM 2023)

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Abstract

The automotive industry is facing the challenge of gaining knowledge from large datasets—originating for example from the research and development process, IT systems, production, or from fleet data. Problems most likely become manifest in data and result in increased costs. Examples can range from problems in the on-board electrical system to virtual validation of autonomous driving functions in R&D.

Hence, one important use case is the automatic detection of anomalous behavior in the data to forecast and identify potential problems as early as possible.

We demonstrate new mathematical methods from Topological Data Analysis (TDA) that can help to address these kinds of problems. TDA is a rather new field in mathematics that combines techniques from geometry and topology to analyze noisy datasets. Beside academia, it has been applied successfully to various fields including medicine (identification of tumor cells), finance (fraud detection), and materials science (structure analysis).

We highlight two main methods from TDA: the (ball) mapper algorithm and persistent homology and illustrate potential applications in automotive industry. We illustrate these abstract methods and show that they can produce valuable knowledge about potential problems—for example in the automotive context—giving an added value to the customer and the OEM.

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Authors and Affiliations

  1. TWT GmbH Science & Innovation, Ernsthaldenstr. 17, 70565, Stuttgart, Germany

    F. Beutenmüller, B. Dierolf, M. Keckeisen, F. Pausinger & P. K. S. Vaudrevange

Authors
  1. F. Beutenmüller
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  2. B. Dierolf
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  3. M. Keckeisen
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  4. F. Pausinger
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  5. P. K. S. Vaudrevange
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Corresponding author

Correspondence to P. K. S. Vaudrevange .

Editor information

Editors and Affiliations

  1. Forschungsinstitut für Kraftfahrwesen, Stuttgart, Deutschland

    André Casal Kulzer

  2. Fahrzeugmotoren Stuttgart, Forschungsinstitut für Kraftfahrwesen, Stuttgart, Baden-Württemberg, Deutschland

    Hans-Christian Reuss

  3. FKFS/IVK, Forschungsinstitut für Kraftfahrwesen, Stuttgart, Baden-Württemberg, Deutschland

    Andreas Wagner

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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Beutenmüller, F., Dierolf, B., Keckeisen, M., Pausinger, F., Vaudrevange, P.K.S. (2023). Topological Data Analysis in Automotive Industry. In: Kulzer, A.C., Reuss, HC., Wagner, A. (eds) 23. Internationales Stuttgarter Symposium. ISSYM 2023. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-42048-2_4

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