  A key challenge for autonomous driving is safe trajectory planning in
cluttered, urban environments with dynamic obstacles, such as pedestrians,
bicyclists, and other vehicles. A reliable prediction of the future
environment, including the behavior of dynamic agents, would allow planning
algorithms to proactively generate a trajectory in response to a rapidly
changing environment. We present a novel framework that predicts the future
occupancy state of the local environment surrounding an autonomous agent by
learning a motion model from occupancy grid data using a neural network. We
take advantage of the temporal structure of the grid data by utilizing a
convolutional long-short term memory network in the form of the PredNet
architecture. This method is validated on the KITTI dataset and demonstrates
higher accuracy and better predictive power than baseline methods.
