  Autonomous navigation requires scene understanding of the action-space to
move or anticipate events. For planner agents moving on the ground plane, such
as autonomous vehicles, this translates 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. In this
work, we study scene understanding in the form of online estimation of semantic
BEV maps using the video input from a single onboard camera. We study three key
aspects of this task, image-level understanding, BEV level understanding, and
the aggregation of temporal information. Based on these three pillars we
propose a novel architecture that combines these three aspects. In our
extensive experiments, we demonstrate that the considered aspects are
complementary to each other for BEV understanding. Furthermore, the proposed
architecture significantly surpasses the current state-of-the-art. Code:
https://github.com/ybarancan/BEV_feat_stitch.
