  With the implementation of the new EU regulation 2022/1426 regarding the
type-approval of the automated driving system (ADS) of fully automated
vehicles, scenario-based testing has gained significant importance in
evaluating the performance and safety of advanced driver assistance systems and
automated driving systems. However, the exploration and generation of concrete
scenarios from a single logical scenario can often lead to a number of similar
or redundant scenarios, which may not contribute to the testing goals.
  This paper focuses on the the goal to reduce the scenario set by clustering
concrete scenarios from a single logical scenario. By employing clustering
techniques, redundant and uninteresting scenarios can be identified and
eliminated, resulting in a representative scenario set. This reduction allows
for a more focused and efficient testing process, enabling the allocation of
resources to the most relevant and critical scenarios. Furthermore, the
identified clusters can provide valuable insights into the scenario space,
revealing patterns and potential problems with the system's behavior.
