  Constructing high-definition (HD) maps is a crucial requirement for enabling
autonomous driving. In recent years, several map segmentation algorithms have
been developed to address this need, leveraging advancements in Bird's-Eye View
(BEV) perception. However, existing models still encounter challenges in
producing realistic and consistent semantic map layouts. One prominent issue is
the limited utilization of structured priors inherent in map segmentation
masks. In light of this, we propose DiffMap, a novel approach specifically
designed to model the structured priors of map segmentation masks using latent
diffusion model. By incorporating this technique, the performance of existing
semantic segmentation methods can be significantly enhanced and certain
structural errors present in the segmentation outputs can be effectively
rectified. Notably, the proposed module can be seamlessly integrated into any
map segmentation model, thereby augmenting its capability to accurately
delineate semantic information. Furthermore, through extensive visualization
analysis, our model demonstrates superior proficiency in generating results
that more accurately reflect real-world map layouts, further validating its
efficacy in improving the quality of the generated maps.
