A Graph-based Framework for Coverage Analysis in Autonomous Driving

TL;DR

This paper introduces a graph-based framework for comprehensive coverage analysis in autonomous driving, capturing complex scene interactions and enabling scalable, scenario-agnostic validation using subgraph matching and graph embeddings.

Contribution

It presents a novel hierarchical graph representation and two complementary analysis methods, improving coverage assessment in diverse traffic scenarios over existing approaches.

Findings

Effective scene representation with hierarchical graphs

Successful validation on real and synthetic data

Enhanced scalability and scenario coverage

Abstract

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.