  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.
