The continuous advancement of autonomous driving (AD) introduces challenges across multiple disciplines to ensure safe and efficient driving. One such challenge is the generation of High-Definition (HD) maps, which must remain up to date and highly accurate for downstream automotive tasks. One promising approach is the use of crowdsourced data from a vehicle fleet, representing road topology and lane-level features. This work focuses on the generation of centerlines and lane dividers from crowdsourced vehicle trajectories. We adopt a Detection Transformer (DETR)-based approach, where a rasterized representation of vehicle trajectories is used as input to predict vectorized lane representations. Each lane consists of a centerline with an associated direction and corresponding lane dividers that are geometrically constrained by the centerline. Our method includes the extraction of local tiles, from which crowdsourced vehicle trajectories are aggregated. Each tile undergoes a transformation into a rasterized representation encoding both the presence and direction of each trajectory, enabling the prediction of vectorized directed lanes. Experiments are conducted on an internal dataset as well as on the public datasets nuScenes and nuPlan.

翻译：自动驾驶技术的持续进步为保障安全高效驾驶带来了多学科领域的挑战，其中高精地图的生成是关键技术之一。高精地图需保持最新状态并具备高精度，以支撑下游汽车任务。一种具有前景的方法是利用车辆车队众包数据，构建道路拓扑结构和车道级特征。本研究聚焦于从众包车辆轨迹中生成车道中心线和车道分隔线。我们采用基于检测Transformer的方法，将车辆轨迹的光栅化表示作为输入，预测矢量化的车道表示。每条车道由带有方向属性的中心线及相关联的车道分隔线构成，且分隔线受中心线的几何约束。该方法包含局部瓦片提取模块，用于聚合众包车辆轨迹数据。每个瓦片经过转换生成光栅化表示，该表示编码了每条轨迹的存在状态和方向信息，从而支持矢量化有向车道的预测。实验在内部数据集以及公开数据集nuScenes和nuPlan上开展。