Systematic Derivation of Generic Scenarios for Cooperative Perception Systems

As one part of encountering present and future mobility challenges, intelligent transportation systems (ITS) have been developed over the years. As the overall goal, such systems aim to improve traffic efficiency and safety. Being an integral part of ITS for the fulfillment of the safety objective, cooperative perception realized by Vehicle-to-Everything communication (V2X) is considered a main contributor to enhancing safety. In recent years, several studies have explored the potential of such systems in safety use cases. As for driving assistance and automation systems, a common way of testing cooperative driving systems relies on the scenario-based testing approach. However, a systematic strategy is necessary to identify scenarios covering the majority of relevant situations for such applications. For defining an extensive set of scenarios, this study comprehensively analyzes the data set of the German In-Depth Accident Study (GIDAS) from the perspective of V2X-Relevance. The proposed methodology shows a systematic approach to identify relevant accident types and derive generic scenarios. In addition, this approach offers the possibility of defining standard test cases for future regulations.