  The high performance of RGB-D based road segmentation methods contrasts with
their rare application in commercial autonomous driving, which is owing to two
reasons: 1) the prior methods cannot achieve high inference speed and high
accuracy in both ways; 2) the different properties of RGB and depth data are
not well-exploited, limiting the reliability of predicted road. In this paper,
based on the evidence theory, an uncertainty-aware symmetric network (USNet) is
proposed to achieve a trade-off between speed and accuracy by fully fusing RGB
and depth data. Firstly, cross-modal feature fusion operations, which are
indispensable in the prior RGB-D based methods, are abandoned. We instead
separately adopt two light-weight subnetworks to learn road representations
from RGB and depth inputs. The light-weight structure guarantees the real-time
inference of our method. Moreover, a multiscale evidence collection (MEC)
module is designed to collect evidence in multiple scales for each modality,
which provides sufficient evidence for pixel class determination. Finally, in
uncertainty-aware fusion (UAF) module, the uncertainty of each modality is
perceived to guide the fusion of the two subnetworks. Experimental results
demonstrate that our method achieves a state-of-the-art accuracy with real-time
inference speed of 43+ FPS. The source code is available at
https://github.com/morancyc/USNet.
