  Autonomous navigation requires structured representation of the road network
and instance-wise identification of the other traffic agents. Since the traffic
scene is defined on the ground plane, this corresponds to scene understanding
in the bird's-eye-view (BEV). However, the onboard cameras of autonomous cars
are customarily mounted horizontally for a better view of the surrounding,
making this task very challenging. In this work, we study the problem of
extracting a directed graph representing the local road network in BEV
coordinates, from a single onboard camera image. Moreover, we show that the
method can be extended to detect dynamic objects on the BEV plane. The
semantics, locations, and orientations of the detected objects together with
the road graph facilitates a comprehensive understanding of the scene. Such
understanding becomes fundamental for the downstream tasks, such as path
planning and navigation. We validate our approach against powerful baselines
and show that our network achieves superior performance. We also demonstrate
the effects of various design choices through ablation studies. Code:
https://github.com/ybarancan/STSU
