  Autonomous driving requires a structured understanding of the surrounding
road network to navigate. One of the most common and useful representation of
such an understanding is done in the form of BEV lane graphs. In this work, we
use the video stream from an onboard camera for online extraction of the
surrounding's lane graph. Using video, instead of a single image, as input
poses both benefits and challenges in terms of combining the information from
different timesteps. We study the emerged challenges using three different
approaches. The first approach is a post-processing step that is capable of
merging single frame lane graph estimates into a unified lane graph. The second
approach uses the spatialtemporal embeddings in the transformer to enable the
network to discover the best temporal aggregation strategy. Finally, the third,
and the proposed method, is an early temporal aggregation through explicit BEV
projection and alignment of framewise features. A single model of this proposed
simple, yet effective, method can process any number of images, including one,
to produce accurate lane graphs. The experiments on the Nuscenes and Argoverse
datasets show the validity of all the approaches while highlighting the
superiority of the proposed method. The code will be made public.
