This research is funded by the German Federal Ministry for Economic Affairs and Energy within the project "SET Level -Simulation-based development and testing of automated driving," a successor project to the PEGASUS project and a project in the PEGASUS family (German abbreviation of Simulationsbasiertes Entwickeln und Testen von automatisiertem Fahren). The results presented in this publication reflect the authors' opinions and not necessarily the opinion of all project participants.

Ensuring the quality of automated driving systems is a major challenge the automotive industry is facing. In this context, quality defines the degree to which an object meets expectations and requirements. Especially, highly automated and automated vehicles at SAE level 4 and 5 will be expected to operate safely in various contexts and complex situations without misconduct. Thus, a systematic approach is needed to show their safe operation. A way to address this challenge is simulation-based testing as pure physical testing is not feasible for automated driving at level 4 and 5 since several billion kilometers of driving are necessary. During simulation-based testing, the data used to evaluate the actual quality of an automated driving system are generated using a simulation. However, to rely on these simulation data, the overall simulation, which also includes its simulation models, must provide a certain quality level. This quality level depends on the intended purpose for which the generated simulation data should be used. Therefore, three categories of quality can be considered: simulation quality (e.g., reliable simulation tool), quality of the automated driving system (e.g., handling of dangerous situations), and scenario quality (e.g., scenario criticality). Quality must be determined and evaluated in various process steps in developing and testing automated driving systems, the overall simulation, and the simulation models used for the simulation. In this paper, we propose a conceptual taxonomy to serve a better understanding of quality in the development and testing process to have a clear separation and insight where further testing is neededboth in terms of automated driving systems and simulation, including their simulation models and scenarios used for testing.

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