  Motion is a dominant cue in automated driving systems. Optical flow is
typically computed to detect moving objects and to estimate depth using
triangulation. In this paper, our motivation is to leverage the existing dense
optical flow to improve the performance of semantic segmentation. To provide a
systematic study, we construct four different architectures which use RGB only,
flow only, RGBF concatenated and two-stream RGB + flow. We evaluate these
networks on two automotive datasets namely Virtual KITTI and Cityscapes using
the state-of-the-art flow estimator FlowNet v2. We also make use of the ground
truth optical flow in Virtual KITTI to serve as an ideal estimator and a
standard Farneback optical flow algorithm to study the effect of noise. Using
the flow ground truth in Virtual KITTI, two-stream architecture achieves the
best results with an improvement of 4% IoU. As expected, there is a large
improvement for moving objects like trucks, vans and cars with 38%, 28% and 6%
increase in IoU. FlowNet produces an improvement of 2.4% in average IoU with
larger improvement in the moving objects corresponding to 26%, 11% and 5% in
trucks, vans and cars. In Cityscapes, flow augmentation provided an improvement
for moving objects like motorcycle and train with an increase of 17% and 7% in
IoU.
