  For a successful market launch of automated vehicles (AVs), proof of their
safety is essential. Due to the open parameter space, an infinite number of
traffic situations can occur, which makes the proof of safety an unsolved
problem. With the so-called scenario-based approach, all relevant test
scenarios must be identified. This paper introduces an approach that finds
particularly challenging scenarios from real driving data (\RDDwo) and assesses
their difficulty using a novel metric. Starting from the highD data, scenarios
are extracted using a hierarchical clustering approach and then assigned to one
of nine pre-defined functional scenarios using rule-based classification. The
special feature of the subsequent evaluation of the concrete scenarios is that
it is independent of the performance of the test vehicle and therefore valid
for all AVs. Previous evaluation metrics are often based on the criticality of
the scenario, which is, however, dependent on the behavior of the test vehicle
and is therefore only conditionally suitable for finding "good" test cases in
advance. The results show that with this new approach a reduced number of
particularly challenging test scenarios can be derived.
