  Semantic image segmentation is an essential component of modern autonomous
driving systems, as an accurate understanding of the surrounding scene is
crucial to navigation and action planning. Current state-of-the-art approaches
in semantic image segmentation rely on pre-trained networks that were initially
developed for classifying images as a whole. While these networks exhibit
outstanding recognition performance (i.e., what is visible?), they lack
localization accuracy (i.e., where precisely is something located?). Therefore,
additional processing steps have to be performed in order to obtain
pixel-accurate segmentation masks at the full image resolution. To alleviate
this problem we propose a novel ResNet-like architecture that exhibits strong
localization and recognition performance. We combine multi-scale context with
pixel-level accuracy by using two processing streams within our network: One
stream carries information at the full image resolution, enabling precise
adherence to segment boundaries. The other stream undergoes a sequence of
pooling operations to obtain robust features for recognition. The two streams
are coupled at the full image resolution using residuals. Without additional
processing steps and without pre-training, our approach achieves an
intersection-over-union score of 71.8% on the Cityscapes dataset.
