  Ensuring validation for highly automated driving poses significant obstacles
to the widespread adoption of highly automated vehicles. Scenario-based testing
offers a potential solution by reducing the homologation effort required for
these systems. However, a crucial prerequisite, yet unresolved, is the
definition and reduction of the test space to a finite number of scenarios. To
tackle this challenge, we propose an extension to a contrastive learning
approach utilizing graphs to construct a meaningful embedding space. Our
approach demonstrates the continuous mapping of scenes using scene-specific
features and the formation of thematically similar clusters based on the
resulting embeddings. Based on the found clusters, similar scenes could be
identified in the subsequent test process, which can lead to a reduction in
redundant test runs.
