A fast lidar-camera fusion method is proposed to detect road in autonomous vehicles. The height data of lidar is transformed to spherical coordinate system to increase the data density. The RGB data of camera is also transformed to spherical coordinate system to match with the lidar data. The amount of data is greatly reduced by spherical coordinate transformation, which results in the fast running time. The transformed images of height, red, green, and blue are input to the CNN. A dilated convolution structure is newly proposed to improve the learning accuracy by expanding the receptive field of CNN. The experimental results using the KITTI data set are finally presented to show the usefulness of the proposed method.