Inverse Universal Traffic Quality -- a Criticality Metric for Crowded Urban Traffic Scenes

TL;DR

This paper introduces the inverse universal traffic quality metric to identify critical urban traffic scenes, enabling safer scenario-based testing by focusing on scene criticality regardless of specific vehicle configurations.

Contribution

The paper proposes a novel, adaptable criticality metric for urban traffic that is independent of predefined vehicle scenarios and compares favorably with existing metrics.

Findings

The metric effectively identifies critical scenes across various urban traffic situations.

It shows comparable or improved performance relative to traditional criticality metrics.

The approach simplifies the analysis of large traffic datasets for safety assessment.

Abstract

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.