  Robust road segmentation is a key challenge in self-driving research. Though
many image-based methods have been studied and high performances in dataset
evaluations have been reported, developing robust and reliable road
segmentation is still a major challenge. Data fusion across different sensors
to improve the performance of road segmentation is widely considered an
important and irreplaceable solution. In this paper, we propose a novel
structure to fuse image and LiDAR point cloud in an end-to-end semantic
segmentation network, in which the fusion is performed at decoder stage instead
of at, more commonly, encoder stage. During fusion, we improve the multi-scale
LiDAR map generation to increase the precision of the multi-scale LiDAR map by
introducing pyramid projection method. Additionally, we adapted the multi-path
refinement network with our fusion strategy and improve the road prediction
compared with transpose convolution with skip layers. Our approach has been
tested on KITTI ROAD dataset and has competitive performance.
