@article{ref1,
title="FollowNet: a comprehensive benchmark for car-following behavior modeling",
journal="Scientific data",
year="2023",
author="Chen, Xianda and Zhu, Meixin and Chen, Kehua and Wang, Pengqin and Lu, Hongliang and Zhong, Hui and Han, Xu and Wang, Xuesong and Wang, Yinhai",
volume="10",
number="1",
pages="e828-e828",
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.<p /> <p>Language: en</p>",
language="en",
issn="2052-4463",
doi="10.1038/s41597-023-02718-7",
url="http://dx.doi.org/10.1038/s41597-023-02718-7"
}