  Knowledge of the road network topology is crucial for autonomous planning and
navigation. Yet, recovering such topology from a single image has only been
explored in part. Furthermore, it needs to refer to the ground plane, where
also the driving actions are taken. This paper aims at extracting the local
road network topology, directly in the bird's-eye-view (BEV), all in a complex
urban setting. The only input consists of a single onboard, forward looking
camera image. We represent the road topology using a set of directed lane
curves and their interactions, which are captured using their intersection
points. To better capture topology, we introduce the concept of \emph{minimal
cycles} and their covers. A minimal cycle is the smallest cycle formed by the
directed curve segments (between two intersections). The cover is a set of
curves whose segments are involved in forming a minimal cycle. We first show
that the covers suffice to uniquely represent the road topology. The covers are
then used to supervise deep neural networks, along with the lane curve
supervision. These learn to predict the road topology from a single input
image. The results on the NuScenes and Argoverse benchmarks are significantly
better than those obtained with baselines. Code:
https://github.com/ybarancan/TopologicalLaneGraph
