Confidence-Gated Topology Reasoning with Fiducial Marker Priors for Occlusion-Robust Lane Graph Prediction

Abstract

Accurate lane topology perception is crucial for safe autonomous driving, yet vision-based models such as BEVFormer and TopoNet degrade under heavy occlusion and other visibility degradations (e.g., ambiguous road markings). Existing approaches augment vision with global priors like Standard Definition (SD) maps, but these rely on precise GNSS localization and global alignment, which can be unreliable in urban canyons, tunnels, or GNSS-denied areas. Fiducial markers provide a complementary alternative: compact infrastructure-embedded tags that encode structurally complete local lane graphs, mitigating blind spots in topology reasoning where visual pipelines fail. However, marker detections are not always reliable—pose estimates may degrade with distance, and detections may be intermittent under occlusion. To address these challenges, we propose a Confidence-Gated Marker Fusion framework that integrates marker-derived priors into BEV features through a dynamic gating mechanism, regulating the contribution of noisy long-range inputs. In addition, we introduce a temporal marker memory that caches and decays reliable priors across frames, propagating topology guidance during short-term detection gaps. Evaluated on a marker-augmented OpenLane-V2 benchmark, our method outperforms both vision-only and SD map-augmented baselines, achieving notable gains (27%) in lane graph completeness and occlusion robustness. These results demonstrate that fiducial marker priors, when fused with vision-based reasoning, provide a practical and reliable pathway toward resilient lane topology prediction in GNSS-denied urban scenarios.