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.

翻译：高清（HD）地图在自动驾驶系统中扮演着关键角色。近年来，已有方法尝试利用车载传感器实时构建高清地图。由于车载传感器固有的局限性——包括对探测距离敏感以及易受附近车辆遮挡——这些方法在复杂场景和远距离探测任务中性能显著下降。本文探索了一个新视角，通过利用卫星地图补充车载传感器来提升高清地图构建效果。我们首先为nuScenes数据集中的每个样本生成了卫星地图瓦片，并发布了补充数据集以供进一步研究。为实现卫星地图与现有方法的更好融合，我们提出了一个层级融合模块，包括特征级融合和BEV级融合。特征级融合由掩码生成器和掩码交叉注意力机制构成，用于精炼车载传感器的特征。BEV级融合通过对齐模块，缓解车载传感器与卫星地图所获特征之间的坐标差异。在增强版nuScenes上的实验结果表明，该模块可无缝集成至三种现有高清地图构建方法中。卫星地图与所提模块显著提升了这些方法在高清地图语义分割和实例检测任务上的性能。