  Making predictions of future frames is a critical challenge in autonomous
driving research. Most of the existing methods for video prediction attempt to
generate future frames in simple and fixed scenes. In this paper, we propose a
novel and effective optical flow conditioned method for the task of video
prediction with an application to complex urban scenes. In contrast with
previous work, the prediction model only requires video sequences and optical
flow sequences for training and testing. Our method uses the rich
spatial-temporal features in video sequences. The method takes advantage of the
motion information extracting from optical flow maps between neighbor images as
well as previous images. Empirical evaluations on the KITTI dataset and the
Cityscapes dataset demonstrate the effectiveness of our method.
