Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with complex lane topologies, out-of-distribution scenarios, or significant occlusions in the image space. Moreover, merging overlapping lane graphs to obtain consistent large-scale graphs remains difficult. To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph. Due to its modular design, our method allows us to address two complementary tasks: predicting ego-respective successor lane graphs from arbitrary vehicle positions using a graph neural network and aggregating these predictions into a consistent global lane graph. Extensive experiments on a large-scale lane graph dataset demonstrate that our approach yields highly accurate lane graphs, even in regions with severe occlusions. The presented approach to graph aggregation proves to eliminate inconsistent predictions while increasing the overall graph quality. We make our large-scale urban lane graph dataset and code publicly available at http://urbanlanegraph.cs.uni-freiburg.de.

翻译：车道图估计是自动驾驶和高清地图学习中一项至关重要且极具挑战性的任务。现有方法无论是使用车载图像还是航拍图像，在处理复杂车道拓扑结构、分布外场景或图像空间中的严重遮挡时都面临困难。此外，融合重叠的车道图以获得一致的大尺度图仍然困难重重。为克服这些挑战，我们提出了一种新颖的自底向上方法，用于从航拍图像中估计车道图，该方法可将多个重叠图聚合为单个一致的图。得益于其模块化设计，我们的方法能够处理两个互补的任务：使用图神经网络从任意车辆位置预测自车视角下的后续车道图，以及将这些预测聚合为一致的全局车道图。在大规模车道图数据集上的广泛实验表明，我们的方法即使在严重遮挡的区域也能生成高精度的车道图。所提出的图聚合方法能够消除不一致的预测，同时提升整体图质量。我们在http://urbanlanegraph.cs.uni-freiburg.de上公开了大规模城市车道图数据集和代码。