ARETE: 基于注意力机制的栅格化编码用于使用HSV变换的众包车辆车队数据拓扑估计
ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data
AI总结：
本文提出ARETE方法，利用HSV变换的众包车辆数据生成高精度道路拓扑图，通过栅格化编码和注意力机制预测车道中心线和分隔线。
摘要：
自动驾驶技术的持续发展带来了多学科挑战，确保安全高效的驾驶。其中，生成高精度（HD）地图是关键，需保持最新和高准确性以支持下游汽车任务。本文采用众包数据生成道路拓扑和车道特征。我们采用基于检测Transformer（DETR）的方法，将车辆轨迹的栅格化表示作为输入，预测矢量化车道表示。每个车道包括带有方向的中心线及其几何约束的车道分隔线。我们的方法包括提取局部瓷砖，从其中聚合众包车辆轨迹。每个瓷砖经过变换，生成编码轨迹存在和方向的栅格化表示，从而预测矢量化方向车道。实验在内部数据集以及公开数据集nuScenes和nuPlan上进行。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
