Scenario-based testing has become a promising approach to overcome the complexity of real-world traffic for safety assurance of automated vehicles. Within scenario-based testing, a system under test is confronted with a set of predefined scenarios. This set shall ensure more efficient testing of an automated vehicle operating in an open context compared to real-world testing. However, the question arises if a scenario catalog can cover the open context sufficiently to allow an argumentation for sufficiently safe driving functions and how this can be proven. Within this paper, a methodology is proposed to argue a sufficient completeness of a scenario concept using a goal structured notation. Thereby, the distinction between completeness and coverage is discussed. For both, methods are proposed for a streamlined argumentation and regarding evidence. These methods are applied to a scenario concept and the inD dataset to prove the usability.

翻译：基于场景的测试已成为应对现实世界交通复杂性、保障自动驾驶车辆安全性的有前景方法。在基于场景的测试中，被测系统需面对一组预定义场景。与真实道路测试相比，该场景集旨在更高效地测试在开放环境中运行的自动驾驶车辆。然而，问题在于场景目录能否充分覆盖开放环境，以支撑自动驾驶功能足够安全的论证，以及如何证明这种覆盖的充分性。本文提出了一种方法论，利用目标结构化表示法论证场景概念的充分完备性。在此基础上，讨论了完备性与覆盖性之间的区别。针对两者，分别提出了简化论证与证据支撑的方法。这些方法被应用于实际场景概念与inD数据集，以验证其可用性。