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For a successful market launch of automated vehicles (AVs), proof of theirsafety is essential. Due to the open parameter space, an infinite number oftraffic situations can occur, which makes the proof of safety an unsolvedproblem. With the so-called scenario-based approach, all relevant testscenarios must be identified. This paper introduces an approach that findsparticularly challenging scenarios from real driving data (\RDDwo) and assessestheir difficulty using a novel metric. Starting from the highD data, scenariosare extracted using a hierarchical clustering approach and then assigned to oneof nine pre-defined functional scenarios using rule-based classification. Thespecial feature of the subsequent evaluation of the concrete scenarios is thatit is independent of the performance of the test vehicle and therefore validfor all AVs. Previous evaluation metrics are often based on the criticality ofthe scenario, which is, however, dependent on the behavior of the test vehicleand is therefore only conditionally suitable for finding "good" test cases inadvance. The results show that with this new approach a reduced number ofparticularly challenging test scenarios can be derived.

Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data