  For an autonomous vehicle it is essential to observe the ongoing dynamics of
a scene and consequently predict imminent future scenarios to ensure safety to
itself and others. This can be done using different sensors and modalities. In
this paper we investigate the usage of optical flow for predicting future
semantic segmentations. To do so we propose a model that forecasts flow fields
autoregressively. Such predictions are then used to guide the inference of a
learned warping function that moves instance segmentations on to future frames.
Results on the Cityscapes dataset demonstrate the effectiveness of optical-flow
methods.
