  Multi-sensor fusion-based road segmentation plays an important role in the
intelligent driving system since it provides a drivable area. The existing
mainstream fusion method is mainly to feature fusion in the image space domain
which causes the perspective compression of the road and damages the
performance of the distant road. Considering the bird's eye views(BEV) of the
LiDAR remains the space structure in horizontal plane, this paper proposes a
bidirectional fusion network(BiFNet) to fuse the image and BEV of the point
cloud. The network consists of two modules: 1) Dense space transformation
module, which solves the mutual conversion between camera image space and BEV
space. 2) Context-based feature fusion module, which fuses the different
sensors information based on the scenes from corresponding features.This method
has achieved competitive results on KITTI dataset.
