  High-definition (HD) maps play a crucial role in autonomous driving systems.
Recent methods have attempted to construct HD maps in real-time using vehicle
onboard sensors. Due to the inherent limitations of onboard sensors, which
include sensitivity to detection range and susceptibility to occlusion by
nearby vehicles, the performance of these methods significantly declines in
complex scenarios and long-range detection tasks. In this paper, we explore a
new perspective that boosts HD map construction through the use of satellite
maps to complement onboard sensors. We initially generate the satellite map
tiles for each sample in nuScenes and release a complementary dataset for
further research. To enable better integration of satellite maps with existing
methods, we propose a hierarchical fusion module, which includes feature-level
fusion and BEV-level fusion. The feature-level fusion, composed of a mask
generator and a masked cross-attention mechanism, is used to refine the
features from onboard sensors. The BEV-level fusion mitigates the coordinate
differences between features obtained from onboard sensors and satellite maps
through an alignment module. The experimental results on the augmented nuScenes
showcase the seamless integration of our module into three existing HD map
construction methods. The satellite maps and our proposed module notably
enhance their performance in both HD map semantic segmentation and instance
detection tasks.
