An essential requirement for scenario-based testing the identification of critical scenes and their associated scenarios. However, critical scenes, such as collisions, occur comparatively rarely. Accordingly, large amounts of data must be examined. A further issue is that recorded real-world traffic often consists of scenes with a high number of vehicles, and it can be challenging to determine which are the most critical vehicles regarding the safety of an ego vehicle. Therefore, we present the inverse universal traffic quality, a criticality metric for urban traffic independent of predefined adversary vehicles and vehicle constellations such as intersection trajectories or car-following scenarios. Our metric is universally applicable for different urban traffic situations, e.g., intersections or roundabouts, and can be adjusted to certain situations if needed. Additionally, in this paper, we evaluate the proposed metric and compares its result to other well-known criticality metrics of this field, such as time-to-collision or post-encroachment time.

翻译：基于场景的测试需要识别关键场景及其相关场景。然而，关键场景（例如碰撞）发生频率相对较低，因此必须对大量数据进行检查。另一个问题是，记录的真实世界交通通常包含高密度车辆的场景，且确定哪些车辆对主车安全最具关键性可能具有挑战性。为此，我们提出了通用交通质量倒数，这是一种独立于预定义对手车辆和车辆组合（如交叉口轨迹或跟车场景）的城市交通关键性度量。我们的度量可普遍适用于不同城市交通场景（例如交叉口或环岛），并且可根据需要针对特定情况进行调整。此外，本文评估了所提出的度量，并将其结果与该领域其他已知的关键性度量（如碰撞时间或后侵占时间）进行了比较。