  For autonomous agents to successfully operate in the real world, anticipation
of future events and states of their environment is a key competence. This
problem can be formalized as a sequence prediction problem, where a number of
observations are used to predict the sequence into the future. However,
real-world scenarios demand a model of uncertainty of such predictions, as
future states become increasingly uncertain and multi-modal -- in particular on
long time horizons. This makes modelling and learning challenging. We cast
state of the art semantic segmentation and future prediction models based on
deep learning into a Bayesian formulation that in turn allows for a full
Bayesian treatment of the prediction problem. We present a new sampling scheme
for this model that draws from the success of variational autoencoders by
incorporating a recognition network. In the experiments we show that our model
outperforms prior work in accuracy of the predicted segmentation and provides
calibrated probabilities that also better capture the multi-modal aspects of
possible future states of street scenes.
