A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network

Road detection is one of the most basic tasks of autonomous driving systems. At present, researches on this issue mainly take two kinds of data as input,
i.e.
, LIDAR point clouds and RGB images from cameras. To make best use of the advantages and bypass the disadvantages of these two kinds of data, we propose a novel network, namely two- stream fusion fully convolutional network (TSF-FCN), which can take advantage of both the accurate location information from LIDAR point clouds and rich appearance information from RGB images. One stream of this network is LIDAR stream which aggregates multi-scale contextual information from LIDAR point clouds. The other stream is RGB stream which is used for extracting features from RGB images. To fuse the two streams, the feature maps of RGB stream are converted to a bird-view representation to concatenate with that of LIDAR stream. In this way, the two kinds of data can complement each other for detecting road. To verify the efficacy of our TSF-FCN, experiments are carried on KITTI- ROAD benchmark and competitive performance is achieved compared with state-of-the-art methods.