解决轨迹数据稀疏与冗余导致的地图碎片化问题
Bridging the Gap Between Sparsity and Redundancy: A Dual-Decoding Framework with Global Context for Map Inference
- 双解码框架融合全局语义与局部几何特征
- 在真实数据集上提升5%的APLS指标
- 适合处理滴滴等平台的海量轨迹数据
轨迹数据因成本低、覆盖广、持续可用，成为自动地图推理的关键资源。但轨迹密度不均常导致稀疏区域道路断裂、密集区域出现冗余路段，给现有方法带来挑战。为此，我们提出DGMap——一种带全局上下文感知的双解码框架，包含多尺度网格编码、掩码增强关键点提取和全局上下文关系预测模块。通过融合全局语义上下文与局部几何特征，提升关键点检测精度，减少稀疏轨迹区域的道路碎片化；同时，全局上下文关系预测模块通过建模长程轨迹模式，抑制密集区域的虚假连接。在三个真实世界数据集上的实验表明，DGMap相比最先进方法在APLS指标上提升5%，尤其在滴滴出行平台的轨迹数据上表现显著。
原文摘要 · Abstract (English)
Trajectory data has become a key resource for automated map in-ference due to its low cost, broad coverage, and continuous availability. However, uneven trajectory density often leads to frag-mented roads in sparse areas and redundant segments in dense regions, posing significant challenges for existing methods. To address these issues, we propose DGMap, a dual-decoding framework with global context awareness, featuring Multi-scale Grid Encoding, Mask-enhanced Keypoint Extraction, and Global Context-aware Relation Prediction. By integrating global semantic context with local geometric features, DGMap improves keypoint detection accuracy to reduce road fragmentation in sparse-trajectory areas. Additionally, the Global Context-aware Relation Prediction module suppresses false connections in dense-trajectory regions by modeling long-range trajectory patterns. Experimental results on three real-world datasets show that DGMap outperforms state-of-the-art methods by 5% in APLS, with notable performance gains on trajectory data from the Didi Chuxing platform
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