Szenario-Optimierung f\"ur die Absicherung von automatisierten und autonomen Fahrsystemen

Abstract

The verification and validation of automated and autonomous driving systems impose a major challenge, especially the identification of suitable test scenarios. This work presents a methodology that adopts metaheuristic search to optimize scenarios. For this, a suitable search space and a suitable fitness function needs to be created. Starting from abstract descriptions of the system's functionality and use cases, parameterized scenarios are derived. The parameters span a search space, in which the suitable scenarios need to be found. Guided by a fitness function, search-based techniques are used to identify those scenarios, in which the system shows its worst behavior. If the derivation of the fitness function is done correctly, an argumentation basis about test completeness and system quality may be achieved. Further, test goal oriented testing with automated test oracles is enabled.