  Semantic segmentation was seen as a challenging computer vision problem few
years ago. Due to recent advancements in deep learning, relatively accurate
solutions are now possible for its use in automated driving. In this paper, the
semantic segmentation problem is explored from the perspective of automated
driving. Most of the current semantic segmentation algorithms are designed for
generic images and do not incorporate prior structure and end goal for
automated driving. First, the paper begins with a generic taxonomic survey of
semantic segmentation algorithms and then discusses how it fits in the context
of automated driving. Second, the particular challenges of deploying it into a
safety system which needs high level of accuracy and robustness are listed.
Third, different alternatives instead of using an independent semantic
segmentation module are explored. Finally, an empirical evaluation of various
semantic segmentation architectures was performed on CamVid dataset in terms of
accuracy and speed. This paper is a preliminary shorter version of a more
detailed survey which is work in progress.
