  Camera-based end-to-end driving neural networks bring the promise of a
low-cost system that maps camera images to driving control commands. These
networks are appealing because they replace laborious hand engineered building
blocks but their black-box nature makes them difficult to delve in case of
failure. Recent works have shown the importance of using an explicit
intermediate representation that has the benefits of increasing both the
interpretability and the accuracy of networks' decisions. Nonetheless, these
camera-based networks reason in camera view where scale is not homogeneous and
hence not directly suitable for motion forecasting. In this paper, we introduce
a novel monocular camera-only holistic end-to-end trajectory planning network
with a Bird-Eye-View (BEV) intermediate representation that comes in the form
of binary Occupancy Grid Maps (OGMs). To ease the prediction of OGMs in BEV
from camera images, we introduce a novel scheme where the OGMs are first
predicted as semantic masks in camera view and then warped in BEV using the
homography between the two planes. The key element allowing this transformation
to be applied to 3D objects such as vehicles, consists in predicting solely
their footprint in camera-view, hence respecting the flat world hypothesis
implied by the homography.
