@article{ref1,
title="Extracting vehicle trajectories from partially overlapping roadside radar",
journal="Sensors (Basel)",
year="2024",
author="Schrader, Maxwell and Hainen, Alexander and Bittle, Joshua",
volume="24",
number="14",
pages="-",
abstract="This work presents a methodology for extracting vehicle trajectories from six partially-overlapping roadside radars through a signalized corridor. The methodology incorporates radar calibration, transformation to the Frenet space, Kalman filtering, short-term prediction, lane-classification, trajectory association, and a covariance intersection-based approach to track fusion. The resulting dataset contains 79,000 fused radar trajectories over a 26-h period, capturing diverse driving scenarios including signalized intersections, merging behavior, and a wide range of speeds. Compared to popular trajectory datasets such as NGSIM and highD, this dataset offers extended temporal coverage, a large number of vehicles, and varied driving conditions. The filtered leader-follower pairs from the dataset provide a substantial number of trajectories suitable for car-following model calibration. The framework and dataset presented in this work has the potential to be leveraged broadly in the study of advanced traffic management systems, autonomous vehicle decision-making, and traffic research.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s24144640",
url="http://dx.doi.org/10.3390/s24144640"
}