HDSVT: High-Density Semantic Vehicle Trajectory Dataset Based on a Cosmopolitan City Bridge

Trajectory prediction is crucial for autonomous driving, necessitating robust models supported by high-quality datasets. Existing datasets often lack long trajectories and high vehicle density, limiting their use in complex scenarios. We introduce the High-Density Semantic Vehicle Trajectory Dataset (HDSVT), collected via UAVs over Guangzhou Bridge. This dataset features higher vehicle density, extended trajectory lengths, and semantic enhancements, promoting in-depth vehicle trajectory analysis. It includes original UAV videos, processed data with (i) pixel coordinates of vehicle trajectory and lane lines in each video, (ii) corresponding geographic coordinates and (iii) semantic promotion for trajectories and motions. The dataset supports diverse driving behaviors and complex interactions, applicable to driving decision-making, traffic control, and long-term small object tracking.