PriorMaskMap: Robust Online Vectorized Map Construction with Biased Priors

Online vectorized map construction provides autonomous ve-hicles with essential, real-time semantic and geometric scene understand-ing. While leveraging prior maps presents an opportunity to improveaccuracy, they are often biased due to outdated information or tem-porary road changes. Existing methods, which typically treat priors asfully reliable or only consider limited noise patterns, suffer severe perfor-mance degradation when these priors are imperfect. To address this, wepropose PriorMaskMap, a robust framework for prior-guided vectorizedmap construction. Its first innovation is a Prior-Aware Confidence Esti-mator (PACE) that evaluates the input rasterized map against currentvisual observations. It assigns confidence scores to regions classified asreliable, erroneous, or blank, creating a foundational mask that guidestwo subsequent branches. In one branch, the Dual-Prior Query Genera-tor (DPQG) selectively filters and embeds both rasterized and vectorizedpriors to initialize informative queries from trustworthy map structures.In the other, the Confidence-Guided Prior Fusion (CGPF) module spa-tially propagates and refines the mask, using the refined version to gov-ern the fusion of BEV features with the prior map, thereby providingrich contextual representations for the map decoder. Evaluation on thenuScenes dataset shows that PriorMaskMap achieves superior mappingprecision over existing approaches and exhibits marked robustness whenprovided with priors containing apparent errors. Our code is released athttps://github.com/healenrens/PriorMaskMap.