  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.
