Skip to main content

2023 | OriginalPaper | Buchkapitel

AI in the Automotive Industry

How AI is Changing the Automotive World

verfasst von : Peter Schlicht

Erschienen in: Work and AI 2030

Verlag: Springer Fachmedien Wiesbaden

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The transition to alternative powertrain systems and the increasing complexity of automotive software poses great challenges for the automotive industry. This is especially true when novel development paradigms profoundly change the previous tradition in terms of product development. AI is such a driver of change, which we will illuminate in this chapter with regard to its influence on the technical development of future vehicle platforms and mobility products. We will address both the product and the development process as well as the company side.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Grechishnikova, D. (2021). Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Science and Reports, 11, 2021. Grechishnikova, D. (2021). Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Science and Reports, 11, 2021.
Zurück zum Zitat Gharib, M., Lollini, P., Botta, M., Amparore, E., Donatelli, S., & Bondavalli, A. (2018). On the safety of automotive systems incorporating machine learning based components: A Position Paper. 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 271–274. Gharib, M., Lollini, P., Botta, M., Amparore, E., Donatelli, S., & Bondavalli, A. (2018). On the safety of automotive systems incorporating machine learning based components: A Position Paper. 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 271–274.
Zurück zum Zitat Schwalbe, G., Knie, B., Sämann, T., Dobberphul, T., Gauerhof, L., Raafatnia, S., & Rocco, V. (2020). Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications. Bd. vol 12235, in Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops. SAFECOMP 2020. Lecture Notes in Computer Science, von Ortmeier F, Schoitsch E, Bitsch F, Ferreira P (Hrsg.), Casimiro A Cham: Springer. Schwalbe, G., Knie, B., Sämann, T., Dobberphul, T., Gauerhof, L., Raafatnia, S., & Rocco, V. (2020). Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications. Bd. vol 12235, in Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops. SAFECOMP 2020. Lecture Notes in Computer Science, von Ortmeier F, Schoitsch E, Bitsch F, Ferreira P (Hrsg.), Casimiro A Cham: Springer.
Zurück zum Zitat SIG, VDA QMC Working Group 13/Automotive (2015). Automotive SPICE Process Assessment/Reference Model. 3.0. SIG, VDA QMC Working Group 13/Automotive (2015). Automotive SPICE Process Assessment/Reference Model. 3.0.
Zurück zum Zitat Silver, D., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (p. 362). Science. Silver, D., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (p. 362). Science.
Zurück zum Zitat Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. nd International Conference on Learning Representations. ICLR. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. nd International Conference on Learning Representations. ICLR.
Metadaten
Titel
AI in the Automotive Industry
verfasst von
Peter Schlicht
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-658-40232-7_29