@article{WU2026, 
author = {Huijuan WU and Zhichao WANG and Yuanyuan SONG and Anjie SUN and Chenhui HUANG and Shibo HAN and Yu WU and Yunjiang RAO},
title = {Real-Time High-Precision Detection of Vehicle Trajectories Using DAS},
year = {2026},
journal = {Photonic Sensors},
volume = {16},
number = {1},
pages = {9560008},
keywords = {DAS, traffic monitoring, vehicle trajectory extraction, speed estimation},
url = {https://www.sciopen.com/article/10.26599/PhoS.2026.9560008},
doi = {10.26599/PhoS.2026.9560008},
abstract = {Fiber-optic distributed acoustic sensing (DAS) offers a promising solution for continuous traffic monitoring; however, its widespread deployment is often hindered by poor signal quality, resulting in fragmented and faint vehicle trajectories. Existing techniques − including conventional signal processing and deep learning models − struggle to accurately reconstruct trajectories and estimate traffic parameters under such challenging conditions. To overcome these limitations, we propose the DAS-hierarchical vehicle estimation network (DAS-HiVENet), an end-to-end framework that fundamentally advances the state-of-the-art through three key innovations: a two-stage preprocessing pipeline for noise suppression and trajectory preservation; a novel generative adversarial network (GAN) with an enhanced U-shaped convolutional neural network (U-net) generator to reconstruct high-fidelity trajectories from degraded inputs; a rotated-you only look once (R-YOLO) detector using oriented bounding boxes to accurately detect slanted trajectories. Extensive field evaluations on multiple expressways confirm that it surpasses existing methods with breakthrough performance: a trajectory intersection over union (IoU) of 0.7076, vehicle counting detection rate of 96.7%, and speed estimation errors as low as 1.422 km/h for the mean absolute error (MAE) and 1.796% for the mean absolute percentage error (MAPE) over 30 minutes. Even in challenging bridge scenarios with severe trajectory adhesion, DAS-HiVENet maintains an over 96% detection rate and under 4% MAPE in speed estimation − significantly outperforming alternatives.}
}