  Aiming for higher-level scene understanding, this work presents a neural
network approach that takes a road-layout map in bird's-eye-view as input, and
predicts a human-interpretable graph that represents the road's topological
layout. Our approach elevates the understanding of road layouts from pixel
level to the level of graphs. To achieve this goal, an image-graph-image
auto-encoder is utilized. The network is designed to learn to regress the graph
representation at its auto-encoder bottleneck. This learning is self-supervised
by an image reconstruction loss, without needing any external manual
annotations. We create a synthetic dataset containing common road layout
patterns and use it for training of the auto-encoder in addition to the
real-world Argoverse dataset. By using this additional synthetic dataset, which
conceptually captures human knowledge of road layouts and makes this available
to the network for training, we are able to stabilize and further improve the
performance of topological road layout understanding on the real-world
Argoverse dataset. The evaluation shows that our approach exhibits comparable
performance to a strong fully-supervised baseline.
