DUMap: Dirichlet Uncertainty-Guided Online Vectorized HD Mapping under Environmental and Geographic Domain Shifts

Online vectorized HD mapping is important for autonomous driving, but exist- ing methods suffer severe performance degradation under environmental domain shifts such as nighttime, rain, and low visibility. Existing methods mainly model uncertainty in geometric outputs, while the evidence-allocation process remains deterministic. Under distribution shifts, a map query can also be uncertain about which sampled BEV evidence should be used. To address this issue, we propose DUMap to model query-to-BEV evidence-allocation uncertainty before geometric prediction. Dirichlet Attention (DA) models the normalized evidence-allocation weights with a Dirichlet distribution. Its mean determines the relative evidence allocation, and its total concentration measures allocation reliability. Uncertainty- Guided Residual Fusion (UGRF) uses this reliability to combine local geometric predictions with global structural refinement. We also introduce Env-Split on nuScenes by excluding nighttime and rainy samples from training. Experiments show that DUMap achieves state-of-the-art (SOTA) performance under environ- mental OOD evaluation while maintaining strong results on region-based and city-based splits.