用车队轨迹生成高精地图车道线，提升自动驾驶地图更新效率
ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data
- 将车辆轨迹转为栅格图像，输入检测变压器预测车道向量
- 在nuScenes和nuPlan上实现92.3%车道线召回率，精度优于传统方法
- 适合做高精地图构建的团队或自动驾驶系统研发者参考
自动驾驶的发展对高精地图的实时性与准确性提出更高要求。本文基于车队共享的车辆轨迹数据，提出一种基于检测变换器（DETR）的方法，通过将轨迹数据转换为包含方向信息的栅格表示，实现车道中心线与分隔线的联合预测。每条车道由具有方向性的中心线及其几何约束的分隔线构成。实验在内部数据集及公开数据集nuScenes、nuPlan上进行，结果表明该方法在车道线召回率达到92.3%，显著优于现有基准方法。该方法有效提升了从动态交通数据中提取道路拓扑结构的效率与精度。
原文摘要 · Abstract (English)
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.
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