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

Di Wen1,2,3, Yiting Zhu1,3, Zhigang Wu1,3

  • 1Intelligent Transportation System Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 518107, China.

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|December 27, 2025
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Summary
This summary is machine-generated.

A new High-Density Semantic Vehicle Trajectory Dataset (HDSVT) was created using drone footage. This dataset offers long-duration, high-density vehicle data for advanced autonomous driving research.

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Area of Science:

  • Computer Vision
  • Robotics
  • Transportation Science

Background:

  • Autonomous driving requires accurate trajectory prediction.
  • Existing datasets lack sufficient vehicle density and trajectory duration.
  • Complex driving scenarios demand more comprehensive data.

Purpose of the Study:

  • Introduce the High-Density Semantic Vehicle Trajectory Dataset (HDSVT).
  • Address limitations of current datasets for autonomous driving research.
  • Provide a rich resource for analyzing complex multi-vehicle interactions.

Main Methods:

  • Collected data using Unmanned Aerial Vehicles (UAVs) over Guangzhou Bridge.
  • Processed UAV videos to extract pixel and geographic coordinates.
  • Incorporated semantic information for trajectories and motions.

Main Results:

  • Developed a dataset with high vehicle density and extended trajectory lengths.
  • Dataset includes pixel and geographic coordinates, lane lines, and semantic data.
  • Captured diverse driving behaviors and complex multi-vehicle interactions.

Conclusions:

  • The HDSVT dataset is valuable for trajectory and motion prediction.
  • Enables research in driving decision-making and traffic management.
  • Facilitates long-term tracking of small objects in complex environments.