# LoRFT: Benchmarking Long-Range Vehicle Trajectory Reconstruction from Fixed Highway Cameras

arXiv: https://arxiv.org/abs/2607.19911 · HTML: https://mlsanity.com/paper/2607.19911/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 24 of 24)

- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking](https://mlsanity.com/paper/1511.04136/) (2015)
- [Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task](https://mlsanity.com/paper/2203.13608/) (2022)
- [Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction](https://mlsanity.com/paper/2303.16005/) (2023)
- [openDD: A Large-Scale Roundabout Drone Dataset](https://mlsanity.com/paper/2007.08463/) (2020)
- [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network](https://mlsanity.com/paper/2002.12609/) (2020)
- [DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways](https://mlsanity.com/paper/2108.00505/) (2021)
- [Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras](https://mlsanity.com/paper/2206.09770/) (2022)
- [Automatic vehicle trajectory data reconstruction at scale](https://mlsanity.com/paper/2212.07907/) (2022)
- [Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian Processes](https://mlsanity.com/paper/2303.02311/) (2023)
- [Goal-based Neural Physics Vehicle Trajectory Prediction Model](https://mlsanity.com/paper/2409.15182/) (2024)
- [MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing](https://mlsanity.com/paper/2208.01862/) (2022)
- [A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture](https://mlsanity.com/paper/2303.06202/) (2023)
- [ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2412.07369/) (2024)
- [Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction](https://mlsanity.com/paper/2603.10597/) (2026)

## Abstract (arXiv)

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.