通过模仿人类驾驶行为，实现多模态数据融合的在线高精地图构建
Driver2Map: Imitating Human Driving for Online High-Definition Map Construction
- 采用两阶段对齐与姿态引导的鸟瞰图融合策略
- 在IoU和AP指标上超越现有方法，提升地图精度
- 适合自动驾驶系统中动态环境下的高精地图更新
高精地图对自动驾驶系统至关重要。在构建过程中，车载多视角相机图像、标准地图和卫星图像提供关键信息。然而，由于这些数据源在模态和视角上的差异，现有方法常难以有效对齐与融合，导致在线高精地图构建仍具挑战。为此，我们提出Driver2Map，一种受人类驾驶员启发的在线高精地图构建模型。不同于仅使用双模态的现有方法，Driver2Map可同时利用三模态信息。我们提出“两阶段对齐”策略以减少不同模态间的空间错位；引入“姿态引导的鸟瞰图融合”模块，利用相机位姿信息自适应加权多视角特征，有效抑制鸟瞰图生成中的跨视角特征重叠；还设计了“预训练地图先验优化”模块，通过学习地图结构先验来优化初始预测，从而在动态遮挡下提升高精地图预测效果。大量实验表明，Driver2Map在IoU和AP指标上均优于现有方法。
原文摘要 · Abstract (English)
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.
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