AI 中文总结
本文提出SceneGTMM框架，通过保角映射场景策略、双图交互架构与CRF增强预测，提升地图匹配的噪声鲁棒性、跨区域迁移性与可解释性，在多源及跨城轨迹匹配中表现优于基线方法。
AI 中文摘要
地图匹配是连接定位数据与高精度道路网络的关键技术，但面临噪声鲁棒性、跨区域迁移性和可解释性方面的挑战。针对现有方法在局部-全局融合、动态道路网络适配及对黑箱模型的依赖等方面的局限，本文提出SceneGTMM——一种基于保角映射场景相对策略的可迁移GNN-Transformer双图交互地图匹配框架。1）基于保角映射的场景相对策略：构建以轨迹为中心的局部坐标系，减少对训练道路网络的依赖，支持跨区域迁移和动态道路网络更新；2）GNN-Transformer双图交互架构：以GNN建模道路图以捕捉局部拓扑约束，以Transformer建模轨迹图以捕捉全局时间依赖关系，跨图注意力实现噪声抑制与语义对齐；3）CRF增强的结构化预测：结合Transformer的全局上下文与CRF的拓扑转移约束，提升路径连通性与鲁棒性。实验表明，SceneGTM在定位误差为16-50米的多源轨迹上准确率超过80%，较HMM提升5.3%；在跨城市迁移场景中，其性能优于MTrajRec、GraphMM和TMM，并通过注意力与相对坐标可视化增强了可解释性。本研究为面向实时交通感知与自动驾驶路径规划的高精度、可迁移地图匹配提供了新范式。
英文摘要
Map matching is a key technology connecting positioning data with high precision road networks, but it faces challenges in noise robustness, cross regional transfer, and interpretability. To addr ess the limitations of existing methods in local global fusion, dynamic road network adaptation, and reliance on black box mod els, this paper proposes SceneGTMM, a transferable GNN Transformer dual graph interaction map matching framework based on a conformal mapping based scene relative strategy. 1) Conformal mapping based scene relative strategy: constructs trajectory centric local coordinate systems to reduce dependence on the training road network, supporting cross regional transfer and dynamic road network updates; 2) GNN Transformer dual graph interaction architecture: a GNN modeled road graph captures local topological constraints, while a Transformer modeled trajectory graph captures global temporal dependencies, and cross graph attention achieves noise suppression and semantic alignment; 3) CRF enhanced structured prediction: combines the global context of the Transformer with the topological transition constraints of CRF to improve path connectivity and robustness. Experiments show that SceneGTM achieves over 80% accuracy on multi source trajectories with positioning errors of 16 50 meters, representing a 5.3% improvement over HMM. In cross city transfer scenarios, it outperforms MTrajRec, GraphMM, and TMM, and enhances interpretability through attention and relative coordinate visualization. This study provides a new paradigm for high precision, transferable map matching for real time traffic perception and autonomous driving path planning.
