Lane graph estimation is a long-standing problem in the context of autonomous driving. Previous works aimed at solving this problem by relying on large-scale, hand-annotated lane graphs, introducing a data bottleneck for training models to solve this task. To overcome this limitation, we propose to use the motion patterns of traffic participants as lane graph annotations. In our AutoGraph approach, we employ a pre-trained object tracker to collect the tracklets of traffic participants such as vehicles and trucks. Based on the location of these tracklets, we predict the successor lane graph from an initial position using overhead RGB images only, not requiring any human supervision. In a subsequent stage, we show how the individual successor predictions can be aggregated into a consistent lane graph. We demonstrate the efficacy of our approach on the UrbanLaneGraph dataset and perform extensive quantitative and qualitative evaluations, indicating that AutoGraph is on par with models trained on hand-annotated graph data. Model and dataset will be made available at redacted-for-review.

翻译：车道图估计是自动驾驶领域长期存在的难题。以往的研究依赖于大规模人工标注的车道图来解决该问题，这为训练模型完成此任务引入了数据瓶颈。为克服这一局限，我们提出利用交通参与者的运动模式作为车道图标注。在AutoGraph方法中，我们采用预训练的目标追踪器收集车辆、卡车等交通参与者的轨迹片段。基于这些轨迹片段的位置，我们仅使用俯视RGB图像即可从初始位置预测后继车道图，无需任何人工监督。在后续阶段，我们展示了如何将各后继预测结果聚合为一致的车道图。我们在UrbanLaneGraph数据集上验证了该方法的有效性，并通过大量定量与定性评估表明，AutoGraph的性能与基于人工标注图数据训练的模型相当。模型与数据集将在匿名评审后公开。