  Lane-level scene annotations provide invaluable data in autonomous vehicles
for trajectory planning in complex environments such as urban areas and cities.
However, obtaining such data is time-consuming and expensive since lane
annotations have to be annotated manually by humans and are as such hard to
scale to large areas. In this work, we propose a novel approach for lane
geometry estimation from bird's-eye-view images. We formulate the problem of
lane shape and lane connections estimation as a graph estimation problem where
lane anchor points are graph nodes and lane segments are graph edges. We train
a graph estimation model on multimodal bird's-eye-view data processed from the
popular NuScenes dataset and its map expansion pack. We furthermore estimate
the direction of the lane connection for each lane segment with a separate
model which results in a directed lane graph. We illustrate the performance of
our LaneGraphNet model on the challenging NuScenes dataset and provide
extensive qualitative and quantitative evaluation. Our model shows promising
performance for most evaluated urban scenes and can serve as a step towards
automated generation of HD lane annotations for autonomous driving.
