The trajectory data of traffic participants (TPs) is a fundamental resource for evaluating traffic conditions and optimizing policies, especially at urban intersections. Although data acquisition using drones is efficient, existing datasets still have limitations in scene representativeness, information richness, and data fidelity. This study introduces FLUID, comprising a fine-grained trajectory dataset that captures dense conflicts at typical urban signalized intersections, and a lightweight, full-pipeline framework for drone-based trajectory processing. FLUID covers three distinct intersection types, with approximately 5 hours of recording time and featuring over 20,000 TPs across 8 categories. Notably, the dataset records an average of 2.8 vehicle conflicts per minute across all scenes, with roughly 15% of all recorded motor vehicles directly involved in these conflicts. FLUID provides comprehensive data, including trajectories, traffic signals, maps, and raw videos. Comparison with the DataFromSky platform and ground-truth measurements validates its high spatio-temporal accuracy. Through a detailed classification of motor vehicle conflicts and violations, FLUID reveals a diversity of interactive behaviors, demonstrating its value for human preference mining, traffic behavior modeling, and autonomous driving research.
@article{arxiv.2509.00497,
title = {FLUID: A Fine-Grained Lightweight Urban Signalized-Intersection Dataset of Dense Conflict Trajectories},
author = {Yiyang Chen and Zhigang Wu and Guohong Zheng and Xuesong Wu and Liwen Xu and Haoyuan Tang and Zhaocheng He and Haipeng Zeng},
journal= {arXiv preprint arXiv:2509.00497},
year = {2026}
}