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- Title
FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling.
- Authors
Chen, Xianda; Zhu, Meixin; Chen, Kehua; Wang, Pengqin; Lu, Hongliang; Zhong, Hui; Han, Xu; Wang, Xuesong; Wang, Yinhai
- 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.
- Subjects
TRAFFIC flow; RESEARCH personnel
- Publication
Scientific Data, 2023, Vol 10, Issue 1, p1
- ISSN
2052-4463
- Publication type
Article
- DOI
10.1038/s41597-023-02718-7