  Future anticipation is of vital importance in autonomous driving and other
decision-making systems. We present a method to anticipate semantic
segmentation of future frames in driving scenarios based on feature-to-feature
forecasting. Our method is based on a semantic segmentation model without
lateral connections within the upsampling path. Such design ensures that the
forecasting addresses only the most abstract features on a very coarse
resolution. We further propose to express feature-to-feature forecasting with
deformable convolutions. This increases the modelling power due to being able
to represent different motion patterns within a single feature map. Experiments
show that our models with deformable convolutions outperform their regular and
dilated counterparts while minimally increasing the number of parameters. Our
method achieves state of the art performance on the Cityscapes validation set
when forecasting nine timesteps into the future.
