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Enhanced technological capabilities render the application of various, increasingly complex, functional concepts for automated driving possible. In the process, the significance of automotive software for a satisfactory driving experience is growing. To benefit from these new opportunities, thorough assessment in early development stages is highly important. It enables manufacturers to focus resources on the most promising concepts. For early assessment, a common approach is to set up vehicles with additional prototyping hardware and perform real world testing. While this approach is essential to assess the look-and-feel of newly developed concepts, its drawbacks are reduced reproducibility and high expenses to achieve a sufficient and balanced sample. To overcome these drawbacks, new flexible, realistic and preferably automated virtual test methods to complement real world verification and validation are especially required during early development phases.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.