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This paper introduces LoRFT, the first open benchmark for long-range vehicle trajectory reconstruction from fixed highway cameras, addressing the challenges of perspective compression and scale decay that hinder accurate tracking. The authors propose Map-RSTNet, a novel map-aware residual sequence-to-sequence model that effectively reconstructs distant trajectories by leveraging road geometry, achieving significant performance improvements over existing methods. Key results show that Map-RSTNet reduces Average Displacement Error (ADE), Final Displacement Error (FDE), and 5-second RMSE by 11.0%, 15.4%, and 10.5%, respectively, highlighting the importance of integrating road-geometry information in trajectory reconstruction tasks.
Road-geometry-aware models can dramatically enhance the accuracy of long-range vehicle trajectory reconstruction, achieving up to 15.4% improvements over traditional methods.
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.
