  Autonomous driving requires efficient reasoning about the location and
appearance of the different agents in the scene, which aids in downstream tasks
such as object detection, object tracking, and path planning. The past few
years have witnessed a surge in approaches that combine the different taskbased
modules of the classic self-driving stack into an End-toEnd(E2E) trainable
learning system. These approaches replace perception, prediction, and sensor
fusion modules with a single contiguous module with shared latent space
embedding, from which one extracts a human-interpretable representation of the
scene. One of the most popular representations is the Birds-eye View (BEV),
which expresses the location of different traffic participants in the ego
vehicle frame from a top-down view. However, a BEV does not capture the
chromatic appearance information of the participants. To overcome this
limitation, we propose a novel representation that captures various traffic
participants appearance and occupancy information from an array of monocular
cameras covering 360 deg field of view (FOV). We use a learned image embedding
of all camera images to generate a BEV of the scene at any instant that
captures both appearance and occupancy of the scene, which can aid in
downstream tasks such as object tracking and executing language-based commands.
We test the efficacy of our approach on synthetic dataset generated from CARLA.
The code, data set, and results can be found at https://rebrand.ly/APP
OCC-results.
