  With the fast development of autonomous driving technologies, there is an
increasing demand for high-definition (HD) maps, which provide reliable and
robust prior information about the static part of the traffic environments. As
one of the important elements in HD maps, road lane centerline is critical for
downstream tasks, such as prediction and planning. Manually annotating
centerlines for road lanes in HD maps is labor-intensive, expensive and
inefficient, severely restricting the wide applications of autonomous driving
systems. Previous work seldom explores the lane centerline detection problem
due to the complicated topology and severe overlapping issues of lane
centerlines. In this paper, we propose a novel method named CenterLineDet to
detect lane centerlines for automatic HD map generation. Our CenterLineDet is
trained by imitation learning and can effectively detect the graph of
centerlines with vehicle-mounted sensors (i.e., six cameras and one LiDAR)
through iterations. Due to the use of the DETR-like transformer network,
CenterLineDet can handle complicated graph topology, such as lane
intersections. The proposed approach is evaluated on the large-scale public
dataset NuScenes. The superiority of our CenterLineDet is demonstrated by the
comparative results. Our code, supplementary materials, and video
demonstrations are available at
\href{https://tonyxuqaq.github.io/projects/CenterLineDet/}{https://tonyxuqaq.github.io/projects/CenterLineDet/}.
