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.

翻译：交通参与者（TPs）的轨迹数据是评估交通状况和优化策略的基础资源，尤其是在城市交叉口。尽管利用无人机进行数据采集效率较高，但现有数据集在场景代表性、信息丰富度和数据保真度方面仍存在局限。本研究提出了FLUID，它包含一个捕获典型城市信号交叉口密集冲突的细粒度轨迹数据集，以及一个用于无人机轨迹处理的轻量级全流程框架。FLUID涵盖三种不同的交叉口类型，记录时长约5小时，包含8个类别超过20,000个交通参与者。值得注意的是，该数据集在所有场景中平均每分钟记录2.8次车辆冲突，所有记录的机动车中约有15%直接参与了这些冲突。FLUID提供了全面的数据，包括轨迹、交通信号、地图和原始视频。与DataFromSky平台及地面实测数据的对比验证了其高时空精度。通过对机动车冲突与违规行为的详细分类，FLUID揭示了交互行为的多样性，展现了其在人类偏好挖掘、交通行为建模和自动驾驶研究方面的价值。