A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map learning primarily focuses on either detecting geometry-based lanelines or perceiving topology relationships of centerlines. Both of these methods ignore the intrinsic relationship of lanelines and centerlines, that lanelines bind centerlines. While simply predicting both types of lane in one model is mutually excluded in learning objective, we advocate lane segment as a new representation that seamlessly incorporates both geometry and topology information. Thus, we introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. Another is an identical initialization strategy for reference points, which enhances the learning of positional priors for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks, \textit{i.e.}, map element detection (+4.8 mAP), centerline perception (+6.9 DET$_l$), and the newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains a real-time inference speed of 14.7 FPS. Code is accessible at https://github.com/OpenDriveLab/LaneSegNet.

翻译：地图作为自动驾驶系统下游应用的关键信息，通常以车道线或中心线表示。然而，现有地图学习文献主要关注基于几何的车道线检测或中心线拓扑关系感知。这两种方法均忽略了车道线与中心线之间的内在关联，即车道线约束中心线。简单地在同一模型中预测两种车道类型会导致学习目标互斥，因此我们提出将车道线段作为新表征，无缝融合几何与拓扑信息。由此引入LaneSegNet——首个端到端映射网络，通过生成车道线段获取道路结构的完整表征。本算法包含两项关键改进：一是车道注意力模块，用于捕获长程特征空间中的关键区域细节；二是参考点相同初始化策略，增强车道注意力位置先验的学习。在OpenLane-V2数据集上，LaneSegNet在三个任务中均显著超越先前方法，即地图元素检测（+4.8 mAP）、中心线感知（+6.9 DET$_l$）以及新定义的车道线段感知（+5.6 mAP）。此外，其推理速度达到14.7 FPS，满足实时需求。代码开源地址：https://github.com/OpenDriveLab/LaneSegNet。