  For autonomous vehicles to proactively plan safe trajectories and make
informed decisions, they must be able to predict the future occupancy states of
the local environment. However, common issues with occupancy prediction include
predictions where moving objects vanish or become blurred, particularly at
longer time horizons. We propose an environment prediction framework that
incorporates environment semantics for future occupancy prediction. Our method
first semantically segments the environment and uses this information along
with the occupancy information to predict the spatiotemporal evolution of the
environment. We validate our approach on the real-world Waymo Open Dataset.
Compared to baseline methods, our model has higher prediction accuracy and is
capable of maintaining moving object appearances in the predictions for longer
prediction time horizons.
