Citation

Schrader M, Hainen A, Bittle J. Sensors (Basel) 2024; 24(14).

Copyright

(Copyright © 2024, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s24144640

PMID

39066038

PMCID

PMC11280909

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.

Language: en

Keywords

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