  Semantic image and video segmentation stand among the most important tasks in
computer vision nowadays, since they provide a complete and meaningful
representation of the environment by means of a dense classification of the
pixels in a given scene. Recently, Deep Learning, and more precisely
Convolutional Neural Networks, have boosted semantic segmentation to a new
level in terms of performance and generalization capabilities. However,
designing Deep Semantic Segmentation models is a complex task, as it may
involve application-dependent aspects. Particularly, when considering
autonomous driving applications, the robustness-efficiency trade-off, as well
as intrinsic limitations - computational/memory bounds and data-scarcity - and
constraints - real-time inference - should be taken into consideration. In this
respect, the use of additional data modalities, such as depth perception for
reasoning on the geometry of a scene, and temporal cues from videos to explore
redundancy and consistency, are promising directions yet not explored to their
full potential in the literature. In this paper, we conduct a survey on the
most relevant and recent advances in Deep Semantic Segmentation in the context
of vision for autonomous vehicles, from three different perspectives:
efficiency-oriented model development for real-time operation, RGB-Depth data
integration (RGB-D semantic segmentation), and the use of temporal information
from videos in temporally-aware models. Our main objective is to provide a
comprehensive discussion on the main methods, advantages, limitations, results
and challenges faced from each perspective, so that the reader can not only get
started, but also be up to date in respect to recent advances in this exciting
and challenging research field.
