  To validate the safety of automated vehicles (AV), scenario-based testing
aims to systematically describe driving scenarios an AV might encounter. In
this process, continuous inputs such as velocities result in an infinite number
of possible variations of a scenario. Thus, metamodels are used to perform
analyses or to select specific variations for examination. However, despite the
safety criticality of AV testing, metamodels are usually seen as a part of an
overall approach, and their predictions are not questioned. This paper analyzes
the predictive performance of Gaussian processes (GP), deep Gaussian processes,
extra-trees, and Bayesian neural networks (BNN), considering four scenarios
with 5 to 20 inputs. Building on this, an iterative approach is introduced and
evaluated, which allows to efficiently select test cases for common analysis
tasks. The results show that regarding predictive performance, the appropriate
selection of test cases is more important than the choice of metamodels.
However, the choice of metamodels remains crucial: Their great flexibility
allows BNNs to benefit from large amounts of data and to model even the most
complex scenarios. In contrast, less flexible models like GPs convince with
higher reliability. Hence, relevant test cases are best explored using scalable
virtual test setups and flexible models. Subsequently, more realistic test
setups and more reliable models can be used for targeted testing and
validation.
