Citation

Chen X, Zhu M, Chen K, Wang P, Lu H, Zhong H, Han X, Wang X, Wang Y. Sci. Data 2023; 10(1): e828.

Copyright

(Copyright © 2023, Nature Publishing Group)

DOI

10.1038/s41597-023-02718-7

PMID

38007562

Abstract

Car-following is a control process in which a following vehicle adjusts its acceleration to keep a safe distance from the lead vehicle. Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. To address this gap and promote the development of microscopic traffic flow modeling, we establish the first public benchmark dataset for car-following behavior modeling. This benchmark consists of more than 80 K car-following events extracted from five public driving datasets under the same criteria. To give an overview of current progress in car-following modeling, we implemented and tested representative baseline models within the benchmark. The established benchmark provides researchers with consistent data formats and metrics for cross-comparing different car-following models, coming with open datasets and codes.

Language: en