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Car-following is a control process in which a following vehicle (FV) adjustsits acceleration to keep a safe distance from the lead vehicle (LV). Recently,there has been a booming of data-driven models that enable more accuratemodeling of car-following through real-world driving datasets. Although thereare several public datasets available, their formats are not always consistent,making it challenging to determine the state-of-the-art models and how well anew model performs compared to existing ones. In contrast, research fields suchas image recognition and object detection have benchmark datasets likeImageNet, Microsoft COCO, and KITTI. To address this gap and promote thedevelopment of microscopic traffic flow modeling, we establish a publicbenchmark dataset for car-following behavior modeling. The benchmark consistsof more than 80K car-following events extracted from five public drivingdatasets using the same criteria. These events cover diverse situationsincluding different road types, various weather conditions, and mixed trafficflows with autonomous vehicles. Moreover, to give an overview of currentprogress in car-following modeling, we implemented and tested representativebaseline models with the benchmark. Results show that the deep deterministicpolicy gradient (DDPG) based model performs competitively with a lower MSE forspacing compared to traditional intelligent driver model (IDM) andGazis-Herman-Rothery (GHR) models, and a smaller collision rate compared tofully connected neural network (NN) and long short-term memory (LSTM) models inmost datasets. The established benchmark will provide researchers withconsistent data formats and metrics for cross-comparing different car-followingmodels, promoting the development of more accurate models. We open-source ourdataset and implementation code inhttps://github.com/HKUST-DRIVE-AI-LAB/FollowNet.

FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling