Lane-level navigation is critical for geographic information systems and navigation-based tasks, offering finer-grained guidance than road-level navigation by standard definition (SD) maps. However, it currently relies on expansive global HD maps that cannot adapt to dynamic road conditions. Recently, online perception (OP) maps have become research hotspots, providing real-time geometry as an alternative, but lack the global topology needed for navigation. To address these issues, Online Navigation Refinement (ONR), a new mission is introduced that refines SD-map-based road-level routes into accurate lane-level navigation by associating SD maps with OP maps. The map-to-map association to handle many-to-one lane-to-road mappings under two key challenges: (1) no public dataset provides lane-to-road correspondences; (2) severe misalignment from spatial fluctuations, semantic disparities, and OP map noise invalidates traditional map matching. For these challenges, We contribute: (1) Online map association dataset (OMA), the first ONR benchmark with 30K scenarios and 2.6M annotated lane vectors; (2) MAT, a transformer with path-aware attention to aligns topology despite spatial fluctuations and semantic disparities and spatial attention for integrates noisy OP features via global context; and (3) NR P-R, a metric evaluating geometric and semantic alignment. Experiments show that MAT outperforms existing methods at 34 ms latency, enabling low-cost and up-to-date lane-level navigation.

翻译：车道级导航对于地理信息系统和基于导航的任务至关重要，它通过标准定义（SD）地图提供比道路级导航更精细的引导。然而，目前该方法依赖于无法适应动态道路条件的昂贵全球高清地图。近年来，在线感知（OP）地图已成为研究热点，其提供实时几何信息作为替代方案，但缺乏导航所需的全局拓扑结构。为解决这些问题，本文引入了在线导航精化（ONR）这一新任务，它通过关联SD地图与OP地图，将基于SD地图的道路级路线精化为准确的车道级导航。地图到地图的关联需处理车道到道路的多对一映射，面临两大关键挑战：（1）尚无公开数据集提供车道到道路的对应关系；（2）空间波动、语义差异以及OP地图噪声导致的严重错位使得传统地图匹配方法失效。针对这些挑战，我们的贡献包括：（1）在线地图关联数据集（OMA），这是首个ONR基准数据集，包含3万个场景和260万个标注的车道向量；（2）MAT，一种具有路径感知注意力的Transformer模型，能够在空间波动和语义差异下对齐拓扑结构，并通过全局上下文整合噪声OP特征的空间注意力机制；（3）NR P-R，一种评估几何与语义对齐的指标。实验表明，MAT在34毫秒延迟下优于现有方法，能够实现低成本且实时更新的车道级导航。