RKGRScen: Road-Network Knowledge Graph Retrieval-Based Scenario Generation for Autonomous Driving Testing

Abstract

Simulation-based scenario testing is central to the safety validation of autonomous driving systems (ADS), where abstract scenarios must be instantiated into executable concrete scenarios. A key challenge in this process is road matching, which aims to identify a concrete site on a map that genuinely satisfies the road-semantic requirements of a scenario. Existing methods often reduce this step to template-based mapping or coarse geometric filtering, which capture coarse road types but fail to meet the finer structural and topological constraints of a scenario, thereby undermining the executability and behavioral fidelity of the generated scenarios. To address this, we propose RKGRScen (Road-Network Knowledge Graph Retrieval-Based Scenario Generation), which formalizes road matching as a semantic location retrieval problem over a road-network knowledge graph. Its indexing module constructs a road-network knowledge graph from OpenDRIVE maps and partitions it into topologically coherent communities annotated with LLM-generated semantic summaries and violation tags, organizing them into a searchable semantic community index. Its instantiation module then grounds each scenario onto concrete road sites through two-level global-to-local retrieval and resolves executable parameters and conflict-point timing with a constraint solver. In a scenario-quality evaluation on a CARLA Town01–Town05 map pool comprising 2649 scenarios, RKGRScen achieves an executability rate of 93.88%, an end-to-end behavior reproduction rate of 72.59%, and a road–environment matching rate of 93.43%. Therefore, RKGRScen can reliably ground high-level scenarios into executable scenarios that satisfy complex road and topological constraints, providing effective support for the safety validation of autonomous driving systems.