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Modeling Languages for Automotive Digital Twins : A Survey Among the German Automotive Industry

Pfeiffer, Jérôme; Fuchß, Dominik ORCID iD icon 1; Kühn, Thomas; Liebhart, Robin; Neumann, Dirk 2; Neimöck, Christer; Seiler, Christian; Koziolek, Anne ORCID iD icon 1; Wortmann, Andreas
1 Institut für Informationssicherheit und Verlässlichkeit (KASTEL), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

The demand for digital twins and suitable modeling techniques in the automotive industry is increasing rapidly. Yet, there is no common understanding of digital twins in automotive, nor are there modeling techniques established to create automotive digital twins. Recent studies on digital twins focus on the analysis of the literature on digital twins for automotive or in general and, thus, neglect the industrial perspective of automotive practitioners. To mitigate this gap between scientific literature and the industrial perspective, we conducted a questionnaire survey among experts in the German automotive industry to identify i) the desired purposes for and capabilities of digital twins and ii) the modeling techniques related to engineering and operating digital twins across the phases of automotive development. To this end, we contacted 189 members of the Software-Defined Car research project and received 96 responses. The results show that digital twins are considered most useful in the usage and support phase of automotive development, representing vehicles as-operated. Moreover, simulation models, source code, and business process models are currently considered the most important models to be integrated into a digital twin alongside the associated, established tools.


Postprint §
DOI: 10.5445/IR/1000174513
Veröffentlicht am 02.10.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 22.09.2024
Sprache Englisch
Identifikator ISBN: 979-8-4007-0504-5
KITopen-ID: 1000174513
Erschienen in MODELS '24: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. Ed.: A. Egyed
Veranstaltung 27th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS 2024), Lienz, Österreich, 22.09.2024 – 27.09.2024
Verlag Association for Computing Machinery (ACM)
Seiten 92-103
Projektinformation SofDCar (BMWK, 19S21002K)
SFB 1608/1 (DFG, DFG KOORD, SFB 1608)
Nachgewiesen in Dimensions
Scopus
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