Coverage analysis is essential for validating the safety of autonomous driving systems, yet existing approaches typically assess coverage factors individually or in limited combinations, struggling to capture the complex interactions inherent in traffic scenes. This paper proposes a graph-based framework for coverage analysis that represents traffic scenes as hierarchical graphs, combining map topology with actor relationships. The framework introduces a two-phase graph construction algorithm that systematically captures spatial relationships between traffic participants, including leading, following, neighboring, and opposing configurations. Two complementary coverage analysis methods are presented. First, a sub-graph isomorphism approach matches traffic scenes against a set of manually defined archetype graphs representing common driving scenarios. Second, a graph embedding approach utilizes Graph Isomorphism Networks with Edge features (GINE) trained via self-supervised contrastive learning to project traffic scenes into a vector space, enabling similarity-based coverage assessment. The framework is validated on both real-world data from the Argoverse 2.0 dataset and synthetic data from the CARLA simulator. The subgraph isomorphism method is used to calculate node coverage percentages using predefined archetypes, while the embedding approach reveals meaningful structure in the latent space suitable for clustering and anomaly detection. The proposed approach offers significant advantages over traditional methods by scaling efficiently to diverse traffic scenarios without requiring scenario-specific handling, and by naturally accommodating varying numbers of actors in a scene.

翻译：覆盖分析对于验证自动驾驶系统的安全性至关重要，然而现有方法通常单独或有限组合地评估覆盖因素，难以捕捉交通场景中固有的复杂交互。本文提出一种基于图的覆盖分析框架，将交通场景表示为分层图，结合地图拓扑与参与者关系。该框架引入一种两阶段图构建算法，系统性地捕捉交通参与者之间的空间关系，包括前导、跟随、相邻及对向等配置。本文提出了两种互补的覆盖分析方法。首先，子图同构方法将交通场景与一组手动定义的、代表常见驾驶场景的原型图进行匹配。其次，图嵌入方法利用通过自监督对比学习训练的带边特征图同构网络（GINE），将交通场景投影到向量空间中，实现基于相似性的覆盖评估。该框架在Argoverse 2.0数据集的真实数据与CARLA模拟器的合成数据上均得到验证。子图同构方法使用预定义原型计算节点覆盖百分比，而嵌入方法则在潜在空间中揭示了适用于聚类和异常检测的有意义结构。与传统方法相比，所提方法具有显著优势：能够高效扩展到多样化的交通场景而无需针对特定场景进行处理，并能自然地适应场景中不同数量的参与者。