From real driving data to concrete test scenarios

We present a holistic approach that takes recorded traffic scenario instances and yields 'good' test scenarios for automated and autonomous driving systems. Such test scenarios are usually generated from scenario types, for which we present an approach that allows measuring both the test case quality and system behavior. Since this requires completeness of the list of scenario types, we provide both a statistical model and a methodological approach to assess completeness. To achieve the latter, we automatically derive scenario types from real data, which complements current manual scenario derivation. We show technical solutions for each of the steps presented.