Topological Analysis of OpenDRIVE Models for Advanced Autonomous Vehicle Simulations

Abstract. Simulation-based testing is essential for autonomous vehicle development and depends on high-fidelity digital road network models. Although prior research focuses on geometric accuracy and semantic completeness of HD maps, topology remains underexplored. Topological inconsistencies, such as disconnected lane segments, invalid predecessor-successor links, or incomplete junction definitions, can invalidate simulations by preventing realistic vehicle navigation and traffic flow. This study presents a formal topological analysis framework for OpenDRIVE models, representing road networks as directed graphs and applying graph-based methods to analyze connectivity and verify consistency. We define connectivity relationships at three hierarchical levels: road-level predecessor-successor relations, lane-level adjacency, and junction-level merging-diverging structures. The framework introduces explicit consistency predicates for topological validity and enables algorithmic verification through adjacency matrix analysis, reachability computation, and connected component decomposition. A Python-based verification system processes OpenDRIVE XML files and evaluates consistency predicates across the network hierarchy. The approach is validated on four datasets: two synthetic scenarios and two real-world maps from Budapest and Karlsruhe. Results reveal widespread topological defects, with error-to-road ratios between 2.68 and 3.94. Lane-level connectivity violations account for 56-61% of errors in production datasets, while the professionally generated Karlsruhe dataset fragments into 467 isolated components despite containing 886 lanes and centimeter-level geometric accuracy. These findings show that topological verification is essential for HD map quality assurance revealing critical structural defects invisible to conventional geometric validation and simulation-based autonomous vehicle testing.