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

Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges