  Semantic segmentation (SS) is an important perception manner for self-driving
cars and robotics, which classifies each pixel into a pre-determined class. The
widely-used cross entropy (CE) loss-based deep networks has achieved
significant progress w.r.t. the mean Intersection-over Union (mIoU). However,
the cross entropy loss can not take the different importance of each class in
an self-driving system into account. For example, pedestrians in the image
should be much more important than the surrounding buildings when make a
decisions in the driving, so their segmentation results are expected to be as
accurate as possible. In this paper, we propose to incorporate the
importance-aware inter-class correlation in a Wasserstein training framework by
configuring its ground distance matrix. The ground distance matrix can be
pre-defined following a priori in a specific task, and the previous
importance-ignored methods can be the particular cases. From an optimization
perspective, we also extend our ground metric to a linear, convex or concave
increasing function $w.r.t.$ pre-defined ground distance. We evaluate our
method on CamVid and Cityscapes datasets with different backbones (SegNet,
ENet, FCN and Deeplab) in a plug and play fashion. In our extenssive
experiments, Wasserstein loss demonstrates superior segmentation performance on
the predefined critical classes for safe-driving.
