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.

本文旨在从自动驾驶的角度探索语义分割问题，首先进行了一般性的分类调查，然后讨论了其在自动驾驶中的应用。其次，列出了将其部署到需要高精度和鲁棒性的安全系统中的挑战，探讨了替代使用独立语义分割模块的不同方法。最后，基于 CamVid 数据集对不同的语义分割架构进行了实证评估。

自动驾驶深度语义分割：分类、路线图和挑战

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

We explore the efficiency of the CRF inference beyond image level semantic
segmentation and perform joint inference in video frames. The key idea is to
combine best of two worlds: semantic co-labeling and more expressive models.
Our formulation enables us to perform inference over ten thousand images within
seconds and makes the system amenable to perform video semantic segmentation
most effectively. On CamVid dataset, with TextonBoost unaries, our proposed
method achieves up to 8% improvement in accuracy over individual semantic image
segmentation without additional time overhead. The source code is available at
this https URL

本研究探讨了基于 CRF 推理在图像级别的语义分割之外，在视频帧中进行联合推理的效率，并结合语义共同标注和更具表现力的模型。在 CamVid 数据集上，通过 TextonBoost unaries，我们的提议方法在没有额外时间开销的情况下，相对于单独的语义图像分割，实现了高达 8% 的精度提升。