  Safe and proactive planning in robotic systems generally requires accurate
predictions of the environment. Prior work on environment prediction applied
video frame prediction techniques to bird's-eye view environment
representations, such as occupancy grids. ConvLSTM-based frameworks used
previously often result in significant blurring and vanishing of moving
objects, thus hindering their applicability for use in safety-critical
applications. In this work, we propose two extensions to the ConvLSTM to
address these issues. We present the Temporal Attention Augmented ConvLSTM
(TAAConvLSTM) and Self-Attention Augmented ConvLSTM (SAAConvLSTM) frameworks
for spatiotemporal occupancy prediction, and demonstrate improved performance
over baseline architectures on the real-world KITTI and Waymo datasets.
