Graph Modeling in Computer Assisted Automotive Development
This work addresses the challenge of managing complex crash safety data for automotive engineers, but it appears incremental as it builds on previous graph modeling efforts without introducing a fundamentally new paradigm.
The paper tackles the problem of organizing and utilizing crash safety data in automotive development by proposing a graph modeling approach that integrates structured and unstructured data sources, enabling searchability, filtering, recommendation, and prediction for crash CAE data.
We consider graph modeling for a knowledge graph for vehicle development, with a focus on crash safety. An organized schema that incorporates information from various structured and unstructured data sources is provided, which includes relevant concepts within the domain. In particular, we propose semantics for crash computer aided engineering (CAE) data, which enables searchability, filtering, recommendation, and prediction for crash CAE data during the development process. This graph modeling considers the CAE data in the context of the R\&D development process and vehicle safety. Consequently, we connect CAE data to the protocols that are used to assess vehicle safety performances. The R\&D process includes CAD engineering and safety attributes, with a focus on multidisciplinary problem-solving. We describe previous efforts in graph modeling in comparison to our proposal, discuss its strengths and limitations, and identify areas for future work.