  Awareness of the road scene is an essential component for both autonomous
vehicles and Advances Driver Assistance Systems and is gaining importance both
for the academia and car companies. This paper presents a way to learn a
semantic-aware transformation which maps detections from a dashboard camera
view onto a broader bird's eye occupancy map of the scene. To this end, a huge
synthetic dataset featuring 1M couples of frames, taken from both car dashboard
and bird's eye view, has been collected and automatically annotated. A
deep-network is then trained to warp detections from the first to the second
view. We demonstrate the effectiveness of our model against several baselines
and observe that is able to generalize on real-world data despite having been
trained solely on synthetic ones.
