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/}.

翻译：随着自动驾驶技术的快速发展，对高清地图的需求日益增长，高清地图可为交通环境的静态部分提供可靠且鲁棒的先验信息。作为高清地图中的重要元素之一，道路车道中心线对于预测和规划等下游任务至关重要。手动标注高清地图中的道路车道中心线耗时、昂贵且效率低下，严重限制了自动驾驶系统的广泛应用。由于车道中心线复杂的拓扑结构和严重的重叠问题，以往的工作很少探讨车道中心线检测问题。本文提出了一种名为CenterLineDet的新方法，用于自动高清地图生成中的车道中心线检测。我们的CenterLineDet通过模仿学习进行训练，能够通过迭代利用车载传感器（即六个摄像头和一个激光雷达）有效检测中心线图。由于采用了类似DETR的Transformer网络，CenterLineDet能够处理复杂的图拓扑结构，例如车道交叉口。所提方法在大规模公开数据集NuScenes上进行了评估，对比结果证明了CenterLineDet的优越性。我们的代码、补充材料和视频演示可在\href{https://tonyxuqaq.github.io/projects/CenterLineDet/}{https://tonyxuqaq.github.io/projects/CenterLineDet/}获取。