  Segmentation of drivable roads and negative obstacles is critical to the safe
driving of autonomous vehicles. Currently, many multi-modal fusion methods have
been proposed to improve segmentation accuracy, such as fusing RGB and depth
images. However, we find that when fusing two modals of data with untrustworthy
features, the performance of multi-modal networks could be degraded, even lower
than those using a single modality. In this paper, the untrustworthy features
refer to those extracted from regions (e.g., far objects that are beyond the
depth measurement range) with invalid depth data (i.e., 0 pixel value) in depth
images. The untrustworthy features can confuse the segmentation results, and
hence lead to inferior results. To provide a solution to this issue, we propose
the Adaptive-Mask Fusion Network (AMFNet) by introducing adaptive-weight masks
in the fusion module to fuse features from RGB and depth images with
inconsistency. In addition, we release a large-scale RGB-depth dataset with
manually-labeled ground truth based on the NPO dataset for drivable roads and
negative obstacles segmentation. Extensive experimental results demonstrate
that our network achieves state-of-the-art performance compared with other
networks. Our code and dataset are available at:
https://github.com/lab-sun/AMFNet.
