Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verifying these systems, predominantly occurring within simulation. In our approach, we use traffic scenes as a starting point (seed-scene) to address the individuality of various highly automated driving functions and to avoid the problems associated with a predefined test traffic scenario. Different highly autonomous driving functions, or their distinct iterations, may display different behaviors under the same operating conditions. To make a generalizable statement about a seed-scene, we simulate possible outcomes based on various behavior profiles. We utilize our lightweight simulation environment and populate it with rule-based and machine learning behavior models for individual actors in the scenario. We analyze resulting scenarios using a variety of criticality metrics. The density distributions of the resulting criticality values enable us to make a profound statement about the significance of a particular scene, considering various eventualities.

翻译：验证高度自动化驾驶功能具有挑战性，需要识别相关测试场景。基于场景的测试将在验证这些系统中发挥重要作用，且主要发生在仿真环境中。在我们的方法中，我们以交通场景作为起点（种子场景），以应对各种高度自动化驾驶功能的个性化需求，并避免与预定义测试交通场景相关的问题。不同的高度自动驾驶功能或其不同迭代版本，在相同运行条件下可能表现出不同的行为。为了对种子场景做出具有普适性的结论，我们基于多种行为档案模拟了可能的结果。我们利用轻量级仿真环境，并为场景中的各个参与者填充基于规则和机器学习的驾驶行为模型。我们使用多种关键性指标分析生成的场景。通过分析生成的关键性指标值的密度分布，我们能够在考虑各种可能性后，对特定场景的重要性做出深刻判断。