Lane Information Perception Network for HD Maps

Lane line is a very important element in HD maps, and map updating based on information can effectively reduce production cost. We use the images obtained by crowdsourcing for information mining. Most of these images are discontinuous and there are no internal or external parameters. However, lane detection algorithms are mostly applied to the vehicle, which are not suitable to detect road changed information. We propose a lane line perception network for information discovery, which directly takes the returned image as input and outputs the number of lane lines, as well as the color and type attributes of each lane. In contrast to previous works, we have solved the gradient explosion problem and specially optimized type segmentation. Finally, the proposed method is applied to mine information about lane changes.