High-Definition (HD) maps play a crucial role in autonomous driving systems. Recent methods have attempted to construct HD maps in real-time based on information obtained from vehicle onboard sensors. However, the performance of these methods is significantly susceptible to the environment surrounding the vehicle due to the inherent limitation of onboard sensors, such as weak capacity for long-range detection. In this study, we demonstrate that supplementing onboard sensors with satellite maps can enhance the performance of HD map construction methods, leveraging the broad coverage capability of satellite maps. For the purpose of further research, we release the satellite map tiles as a complementary dataset of nuScenes dataset. Meanwhile, we propose a hierarchical fusion module that enables better fusion of satellite maps information with existing methods. Specifically, we design an attention mask based on segmentation and distance, applying the cross-attention mechanism to fuse onboard Bird's Eye View (BEV) features and satellite features in feature-level fusion. An alignment module is introduced before concatenation in BEV-level fusion to mitigate the impact of misalignment between the two features. The experimental results on the augmented nuScenes dataset showcase the seamless integration of our module into three existing HD map construction methods. It notably enhances 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。近期研究尝试基于车载传感器获取的信息实时构建高清地图，然而，由于车载传感器固有的局限性（如远距离探测能力较弱），这些方法的性能极易受车辆周围环境影响。本研究表明，利用卫星地图的广域覆盖能力，通过补充车载传感器信息可提升高清地图构建方法的性能。为便于进一步研究，我们发布了作为nuScenes数据集补充数据的卫星地图瓦片集。同时，提出一种分层融合模块，能更有效地将卫星地图信息与现有方法融合。具体而言，我们设计了基于分割与距离的注意力掩码，通过交叉注意力机制在特征级融合中结合车载鸟瞰图（BEV）特征与卫星特征；在BEV级融合中引入对齐模块，在特征拼接前减轻两者错位的影响。在增强版nuScenes数据集上的实验表明，该模块可无缝集成至三种现有高清地图构建方法，并在高清地图语义分割与实例检测任务中显著提升其性能。