  Car-following is a control process in which a following vehicle (FV) adjusts
its acceleration to keep a safe distance from the lead vehicle (LV). 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. In contrast, research fields such
as image recognition and object detection have benchmark datasets like
ImageNet, Microsoft COCO, and KITTI. To address this gap and promote the
development of microscopic traffic flow modeling, we establish a public
benchmark dataset for car-following behavior modeling. The benchmark consists
of more than 80K car-following events extracted from five public driving
datasets using the same criteria. These events cover diverse situations
including different road types, various weather conditions, and mixed traffic
flows with autonomous vehicles. Moreover, to give an overview of current
progress in car-following modeling, we implemented and tested representative
baseline models with the benchmark. Results show that the deep deterministic
policy gradient (DDPG) based model performs competitively with a lower MSE for
spacing compared to traditional intelligent driver model (IDM) and
Gazis-Herman-Rothery (GHR) models, and a smaller collision rate compared to
fully connected neural network (NN) and long short-term memory (LSTM) models in
most datasets. The established benchmark will provide researchers with
consistent data formats and metrics for cross-comparing different car-following
models, promoting the development of more accurate models. We open-source our
dataset and implementation code in
https://github.com/HKUST-DRIVE-AI-LAB/FollowNet.
