发表机构
School of Transportation, Changsha University of Science and Technology（长沙理工大学交通运输工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对从固定高速公路摄像头恢复长距离车辆轨迹的难题，提出Map-RSTNet模型，引入LoRFT基准测试。实验表明该模型能有效降低误差，证明道路几何感知重建的有效性，为长距离车辆轨迹重建提供了可重复测试平台。
AI 中文摘要
长距离车辆轨迹为交通安全分析、自动驾驶评估和数据驱动的交通管理提供重要时空证据，但从固定高速公路摄像头持续恢复轨迹仍很困难。车辆驶远时，透视压缩和尺度衰减常使自动轨迹片段化或过早终止。本文将问题表述为从可靠近场轨迹恢复车辆轨迹的远距离延续。引入LoRFT基准测试，包含22个高速公路监控场景等数据。还提出Map-RSTNet模型，在LoRFT上该模型相对最强基线分别降低ADE、FDE和5秒RMSE的11.0%、15.4%和10.5%，证明道路几何感知重建可扩展现有固定摄像头基础设施的可用轨迹记录，LoRFT为长距离车辆轨迹重建提供了可重复测试平台。
英文摘要
Long-range vehicle trajectories provide important spatio-temporal evidence for traffic safety analysis, autonomous driving evaluation, and data-driven traffic management, yet continuously recovering them from fixed highway cameras remains difficult. As vehicles recede into distant road regions, perspective compression and scale decay often fragment or prematurely terminate automatic tracklets, even when their continuation remains identifiable from motion consistency across neighboring frames. We formulate this problem as recovering the far-range continuation of a vehicle trajectory from a reliable near-field tracklet. We introduce LoRFT, to our knowledge the first open benchmark dedicated to long-range vehicle trajectory reconstruction from fixed highway cameras. LoRFT comprises 22 expressway surveillance scenes, 366,109 video frames, 6,601 manually verified trajectories, 2,694,889 bounding boxes, road-geometry annotations, scene-level splits, and evaluation scripts. We further propose Map-RSTNet, a map-aware residual sequence-to-sequence model that reconstructs distant trajectories in a road-geometry-aligned state space and dynamically refreshes local road geometry during decoding. On LoRFT, Map-RSTNet reduces ADE, FDE, and 5-second RMSE by 11.0%, 15.4%, and 10.5%, respectively, relative to the strongest baseline. These results demonstrate that road-geometry-aware reconstruction can extend usable trajectory records from existing fixed-camera infrastructure. LoRFT provides a reproducible testbed for long-range vehicle trajectory reconstruction.
Comments17 pages, 5 figures. Code and processed annotations: https://github.com/YvfanZhu/LoRFT
