用轻量非可见车道线引导在线高精地图构建，提升长距感知可靠性。
D2HDMap: Non-visible Driveline Map Prior for Online Vectorized HD Map Prediction
- 引入非可见车道线作为轻量地图先验，指导可见道路结构估计
- 在nuScenes和Argoverse 2上达到44.8 mAP，跨区域测试超越现有方法
- 训练时加入噪声模拟，显著增强对定位误差的鲁棒性
准确、实时的道路结构表征对自动驾驶安全至关重要。现有系统或依赖昂贵且难维护的高精（HD）地图（过时时存在安全隐患），或完全依赖传感器的在线建图（受远距精度与遮挡影响）。融合地图先验的在线建图系统试图结合两者优势。本文提出D2HDMap，一种在线建图系统，通过注入轻量级、非可见的车道线先验，引导对车道分隔线、道路边界和人行横道等可见道路结构的估计。该先验相比完整HD地图更易构建与更新。实验表明，使用此先验训练的模型在推理时即使无先验仍能保持良好性能。在nuScenes与Argoverse 2数据集上的消融实验显示，模型在无先验条件下仍具强鲁棒性；在地理隔离划分下，D2HDMap取得44.8 mAP，超越当前最优。此外，噪声感知训练显著提升对真实定位误差的鲁棒性。
原文摘要 · Abstract (English)
Accurate, up-to-date representations of road structures are critical for the safe operation of autonomous vehicles. Existing systems rely either on costly, maintenance-heavy high-definition (HD) maps which compromise safety when outdated, or purely sensor-based online mapping which struggles with long-range reliability and occlusion. Systems incorporating map prior information into online mapping seek to overcome drawbacks of both approaches by combining them in some way. We propose 'Driveline To HD Map' (D2HDMap), an online mapping system that injects a lightweight, non-visible driveline prior to guide the estimation of visible road structures such as lane dividers, road boundaries and crosswalks. This prior incurs less effort to create and update compared to full HD map priors used in other approaches. We also show that training with such a prior can improve generalization at inference time when no prior is available. Ablation studies conducted on the nuScenes and Argoverse 2 dataset demonstrate that models trained using a driveline prior largely retain performance even when priors are not available. On a geographically disjoint split, D2HDMap achieves 44.8 mAP, surpassing recent state-of-the-art. Additionally, noise-aware training substantially increases robustness to realistic localization error.
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