  We present a self-supervised approach to estimate flow in camera image and
top-view grid map sequences using fully convolutional neural networks in the
domain of automated driving. We extend existing approaches for self-supervised
optical flow estimation by adding a regularizer expressing motion consistency
assuming a static environment. However, as this assumption is violated for
other moving traffic participants we also estimate a mask to scale this
regularization. Adding a regularization towards motion consistency improves
convergence and flow estimation accuracy. Furthermore, we scale the errors due
to spatial flow inconsistency by a mask that we derive from the motion mask.
This improves accuracy in regions where the flow drastically changes due to a
better separation between static and dynamic environment. We apply our approach
to optical flow estimation from camera image sequences, validate on odometry
estimation and suggest a method to iteratively increase optical flow estimation
accuracy using the generated motion masks. Finally, we provide quantitative and
qualitative results based on the KITTI odometry and tracking benchmark for
scene flow estimation based on grid map sequences. We show that we can improve
accuracy and convergence when applying motion and spatial consistency
regularization.
