  Semantic Segmentation (SS) is a task to assign semantic label to each pixel
of the images, which is of immense significance for autonomous vehicles,
robotics and assisted navigation of vulnerable road users. It is obvious that
in different application scenarios, different objects possess hierarchical
importance and safety-relevance, but conventional loss functions like cross
entropy have not taken the different levels of importance of diverse traffic
elements into consideration. To address this dilemma, we leverage and re-design
an importance-aware loss function, throwing insightful hints on how importance
of semantics are assigned for real-world applications. To customize semantic
segmentation networks for different navigational tasks, we extend ERF-PSPNet, a
real-time segmenter designed for wearable device aiding visually impaired
pedestrians, and propose BiERF-PSPNet, which can yield high-quality
segmentation maps with finer spatial details exceptionally suitable for
autonomous vehicles. A comprehensive variety of experiments with these
efficient pyramidal context networks on CamVid and Cityscapes datasets
demonstrates the effectiveness of our proposal to support diverse navigational
assistant systems.
