  Joint scene understanding and segmentation for automotive applications is a
challenging problem in two key aspects:- (1) classifying every pixel in the
entire scene and (2) performing this task under unstable weather and
illumination changes (e.g. foggy weather), which results in poor outdoor scene
visibility. This poor outdoor scene visibility leads to a non-optimal
performance of deep convolutional neural network-based scene understanding and
segmentation. In this paper, we propose an efficient end-to-end contemporary
automotive semantic scene understanding approach under foggy weather
conditions, employing domain adaptation and illumination-invariant image
per-transformation. As a multi-task pipeline, our proposed model provides:- (1)
transferring images from extreme to clear-weather condition using domain
transfer approach and (2) semantically segmenting a scene using a competitive
encoder-decoder convolutional neural network (CNN) with dense connectivity,
skip connections and fusion-based techniques. We evaluate our approach on
challenging foggy datasets, including synthetic dataset (Foggy Cityscapes) as
well as real-world datasets (Foggy Zurich and Foggy Driving). By incorporating
RGB, depth, and illumination-invariant information, our approach outperforms
the state-of-the-art within automotive scene understanding, under foggy weather
condition.
