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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Extracting Vehicle Trajectories from Partially Overlapping Roadside Radar.

Maxwell Schrader¹, Alexander Hainen¹, Joshua Bittle¹

¹Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

This study introduces a new method to extract vehicle trajectories using roadside radars, creating a large dataset for traffic research. The fused radar trajectories capture diverse driving scenarios for advanced traffic management and autonomous vehicle studies.

Keywords:

car-following model data fusion driver behavior partially-overlapping fusion trajectory calibration

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Area of Science:

Traffic Engineering
Computer Vision
Data Science

Background:

Accurate vehicle trajectory data is crucial for traffic flow analysis and autonomous systems.
Existing datasets often lack sufficient temporal coverage or diversity in driving conditions.

Purpose of the Study:

To develop a robust methodology for extracting and fusing vehicle trajectories from multiple roadside radars.
To create a comprehensive dataset of fused radar trajectories for traffic research.

Main Methods:

Radar calibration and transformation to Frenet space.
Kalman filtering, short-term prediction, and lane classification.
Trajectory association and covariance intersection-based track fusion.

Main Results:

A dataset of 79,000 fused radar trajectories over 26 hours.
Capture of diverse driving scenarios including intersections and merging.
Identification of numerous leader-follower pairs for car-following model calibration.

Conclusions:

The presented framework and dataset offer significant advantages over existing resources.
The methodology and data are valuable for advancing traffic management and autonomous vehicle research.