  We investigate the multi-step prediction of the drivable space, represented
by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that
accurate multi-step prediction of the drivable space can efficiently improve
path planning and navigation resulting in safe, comfortable and optimum paths
in autonomous driving. We train a variety of Recurrent Neural Network (RNN)
based architectures on the OGM sequences from the KITTI dataset. The results
demonstrate significant improvement of the prediction accuracy using our
proposed difference learning method, incorporating motion related features,
over the state of the art. We remove the egomotion from the OGM sequences by
transforming them into a common frame. Although in the transformed sequences
the KITTI dataset is heavily biased toward static objects, by learning the
difference between subsequent OGMs, our proposed method provides accurate
prediction over both the static and moving objects.
