🤖 AI Summary
This study addresses the challenge of continuously reconstructing long-range vehicle trajectories from fixed highway cameras, which is hindered by perspective compression and scale attenuation. To this end, the authors introduce LoRFT, the first open benchmark specifically designed for this task, and propose Map-RSTNet, a map-aware sequence-to-sequence model. Map-RSTNet dynamically integrates local road structure within a road-geometry-aligned state space, leveraging a residual architecture and a geometric refresh mechanism to sustain trajectory continuity. Experimental results demonstrate that Map-RSTNet significantly outperforms existing methods on LoRFT, reducing Average Displacement Error (ADE), Final Displacement Error (FDE), and 5-second RMSE by 11.0%, 15.4%, and 10.5%, respectively, thereby effectively extending the usable length of trajectories captured by fixed cameras.
📝 Abstract
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.