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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; Neimöck, Christer; Seiler, Christian; Koziolek, Anne ORCID iD icon 1; Wortmann, Andreas
1 Institut für Informationssicherheit und Verlässlichkeit (KASTEL), Karlsruher Institut für Technologie (KIT)

Abstract:

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.


Volltext §
DOI: 10.5445/IR/1000173318
Veröffentlicht am 12.08.2024
Originalveröffentlichung
DOI: 10.1145/3640310.3674100
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2024
Sprache Englisch
Identifikator KITopen-ID: 1000173318
Verlag Association for Computing Machinery (ACM)
Projektinformation SofDCar (BMWK, 19S21002K)
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