TopoHG: Reliable Hypergraph Reasoning for Lane Topology

Inferring a structured road graph from surround-view images requires jointly de- tecting 3D lane centerlines and traffic elements, and predicting lane-to-lane and lane-to-traffic-element relations. Most query-based methods first detect individ- ual road elements and then predict topological relations for candidate element pairs. Under this paradigm, false-positive lane queries may still receive high confidence, allowing detection errors to propagate into topology prediction and induce spurious links. Moreover, pairwise prediction does not explicitly capture group-level topology patterns spanning multiple road elements, such as lane splits, merges, and multi-lane control associations. To address these limitations, we pro- pose TopoHG, a reliable-query-driven topology reasoning framework based on a structure-aware heterogeneous hypergraph. TopoHG combines contrastive query learning with online hard-negative mining to produce informative and discrimina- tive lane-query representations. The resulting reliability estimates softly weight lane queries during hypergraph construction, limiting the propagation of unreli- able candidates into topology reasoning. TopoHG then constructs structure-aware hypergraphs over lane and traffic-element queries, aligning with real-world split, merge, and control patterns. By integrating query semantics, lane geometry, and coarse pairwise predictions, cross-hyperedge interaction and gated bidirectional node–hyperedge message passing enable context sharing within and across struc- tural groups. The resulting group-level context is propagated back to individual queries to refine L2L and L2T relation predictions. On OpenLane-V2, TopoHG achieves OLS scores of 50.71 on Subset-A and 48.20 on Subset-B, with consistent gains across element detection and topology tasks.