  We address one of the crucial aspects necessary for safe and efficient
operations of autonomous vehicles, namely predicting future state of traffic
actors in the autonomous vehicle's surroundings. We introduce a deep
learning-based approach that takes into account a current world state and
produces raster images of each actor's vicinity. The rasters are then used as
inputs to deep convolutional models to infer future movement of actors while
also accounting for and capturing inherent uncertainty of the prediction task.
Extensive experiments on real-world data strongly suggest benefits of the
proposed approach. Moreover, following completion of the offline tests the
system was successfully tested onboard self-driving vehicles.
