  Reliably predicting future occupancy of highly dynamic urban environments is
an important precursor for safe autonomous navigation. Common challenges in the
prediction include forecasting the relative position of other vehicles,
modelling the dynamics of vehicles subjected to different traffic conditions,
and vanishing surrounding objects. To tackle these challenges, we propose a
spatio-temporal prediction network pipeline that takes the past information
from the environment and semantic labels separately for generating future
occupancy predictions. Compared to the current SOTA, our approach predicts
occupancy for a longer horizon of 3 seconds and in a relatively complex
environment from the nuScenes dataset. Our experimental results demonstrate the
ability of spatio-temporal networks to understand scene dynamics without the
need for HD-Maps and explicit modeling dynamic objects. We publicly release our
occupancy grid dataset based on nuScenes to support further research.
