  In this paper, we train a recurrent neural network to learn dynamics of a
chaotic road environment and to project the future of the environment on an
image. Future projection can be used to anticipate an unseen environment for
example, in autonomous driving. Road environment is highly dynamic and complex
due to the interaction among traffic participants such as vehicles and
pedestrians. Even in this complex environment, a human driver is efficacious to
safely drive on chaotic roads irrespective of the number of traffic
participants. The proliferation of deep learning research has shown the
efficacy of neural networks in learning this human behavior. In the same
direction, we investigate recurrent neural networks to understand the chaotic
road environment which is shared by pedestrians, vehicles (cars, trucks,
bicycles etc.), and sometimes animals as well. We propose \emph{Foresee}, a
unidirectional gated recurrent units (GRUs) network with attention to project
future of the environment in the form of images. We have collected several
videos on Delhi roads consisting of various traffic participants, background
and infrastructure differences (like 3D pedestrian crossing) at various times
on various days. We train \emph{Foresee} in an unsupervised way and we use
online training to project frames up to $0.5$ seconds in advance. We show that
our proposed model performs better than state of the art methods (prednet and
Enc. Dec. LSTM) and finally, we show that our trained model generalizes to a
public dataset for future projections.
