High-definition (HD) semantic maps are crucial for autonomous vehicles navigating urban environments. Traditional offline HD maps, created through labor-intensive manual annotation processes, are both costly and incapable of accommodating timely updates. Recently, researchers have proposed inferring local maps based on online sensor observations; however, this approach is constrained by the sensor perception range and is susceptible to occlusions. In this work, we propose Neural Map Prior (NMP), a neural representation of global maps that facilitates automatic global map updates and improves local map inference performance. To incorporate the strong map prior into local map inference, we employ cross-attention that dynamically captures correlations between current features and prior features. For updating the global neural map prior, we use a learning-based fusion module to guide the network in fusing features from previous traversals. This design allows the network to capture a global neural map prior during sequential online map predictions. Experimental results on the nuScenes dataset demonstrate that our framework is highly compatible with various map segmentation and detection architectures and considerably strengthens map prediction performance, even under adverse weather conditions and across longer horizons. To the best of our knowledge, this represents the first learning-based system for constructing a global map prior.

翻译：高清（HD）语义地图对于在城市环境中导航的自动驾驶车辆至关重要。传统的离线高清地图通过耗时的人工标注过程创建，不仅成本高昂，而且无法及时更新。近期，研究人员提出基于在线传感器观测来推断局部地图；然而，该方法受限于传感器感知范围，且易受遮挡影响。本研究提出神经地图先验（Neural Map Prior, NMP），这是一种全局地图的神经表征，能够促进全局地图自动更新并提升局部地图推断性能。为将强地图先验融入局部地图推断，我们采用交叉注意力机制动态捕获当前特征与先验特征之间的关联。为更新全局神经地图先验，我们使用基于学习的融合模块引导网络融合先前遍历中的特征。此设计使网络能在顺序在线地图预测过程中捕获全局神经地图先验。在nuScenes数据集上的实验结果表明，我们的框架与各类地图分割与检测架构高度兼容，即便在恶劣天气条件和更长时域下，也能显著增强地图预测性能。据我们所知，这是首个基于学习的全局地图先验构建系统。