  In self driving car applications, there is a requirement to predict the
location of the lane given an input RGB front facing image. In this paper, we
propose an architecture that allows us to increase the speed and robustness of
road detection without a large hit in accuracy by introducing an underlying
shared feature space that is propagated over time, which serves as a flowing
dynamic memory. By utilizing the gist of previous frames, we train the network
to predict the current road with a greater accuracy and lesser deviation from
previous frames.
