外观
论文arXiv 2608.013382 个月前
Driver2Map：模仿人类驾驶的在线高精地图构建
原文链接：Driver2Map: Imitating Human Driving for Online High-Definition Map Construction（arXiv 2608.01338 · 发布日期：2026-08-02）
Driver2Map: Imitating Human Driving for Online High-Definition Map Construction
Driver2Map面向在线高精地图构建，融合车载多视角图像、标准精度地图和卫星图，并通过对齐与BEV融合在实验中验证。
核心要点
- 现有方法难以对齐和融合不同模态、不同视角数据，在线高精地图构建仍具挑战。
- Driver2Map同时利用三种模态，采用两阶段对齐降低跨模态空间错位。
- Pose-Guided BEV Fusion利用相机位姿自适应加权多视角特征，抑制跨视角重叠。
- 实验显示Driver2Map在IoU和AP指标上优于现有方法；摘要未提及开源。
验证范围与局限：验证范围为在线高精地图构建实验，摘要未说明具体数据集或实车部署。
英文摘要 (Abstract)
High-definition (HD) maps are essential for autonomous driving systems. In constructing such maps, onboard multi-view camera images, standard-definition maps and satellite images provide crucial information. However, due to the modality and perspective differences among these data sources, existing methods often struggle to effectively align and fuse them, making online HD map construction still challenging. To address these issues, we propose Driver2Map, an online HD map construction model inspired by human drivers. Unlike existing HD map construction models that utilize only two modalities, our Driver2Map can simultaneously exploit three modalities. Specifically, we propose a "two-stage alignment" strategy to reduce spatial misalignment across different modalities. Additionally, we introduce "Pose-Guided BEV Fusion", a BEV (bird's-eye-view) generation module that leverages camera pose information to adaptively weight multi-view features, thereby effectively suppressing cross-view feature overlap during BEV generation. Also, we design a "Pretrained Prior for Map Refinement" module to refine the initial prediction by learning map structure priors, thus improving the HD map prediction under dynamic occlusions. Extensive experiments demonstrate that Driver2Map outperforms existing methods on both IoU and AP metrics.
作者：Pan Yin, Runtian Xia, Weisong Kuang, Kaiyu Li, Cong Zhao, Xiangyong Cao
来源
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