Car-following (CF) modeling, a fundamental component in microscopic traffic
simulation, has attracted increasing interest of researchers in the past
decades. In this study, we propose an adaptable personalized car-following
framework -MetaFollower, by leveraging the power of meta-learning.
Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract
common driving knowledge from various CF events. Afterward, the pre-trained
model can be fine-tuned on new drivers with only a few CF trajectories to
achieve personalized CF adaptation. We additionally combine Long Short-Term
Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal
heterogeneity with high interpretability. Unlike conventional adaptive cruise
control (ACC) systems that rely on predefined settings and constant parameters
without considering heterogeneous driving characteristics, MetaFollower can
accurately capture and simulate the intricate dynamics of car-following
behavior while considering the unique driving styles of individual drivers. We
demonstrate the versatility and adaptability of MetaFollower by showcasing its
ability to adapt to new drivers with limited training data quickly. To evaluate
the performance of MetaFollower, we conduct rigorous experiments comparing it
with both data-driven and physics-based models. The results reveal that our
proposed framework outperforms baseline models in predicting car-following
behavior with higher accuracy and safety. To the best of our knowledge, this is
the first car-following model aiming to achieve fast adaptation by considering
both driver and temporal heterogeneity based on meta-learning.

通过元学习，我们提出了一种可适应个性化车辆跟驰的 MetaFollower 框架，结合长短期记忆网络（LSTM）和智能驾驶模型（IDM），能够准确捕捉和模拟车辆跟驰行为的细微动态，并考虑到个体驾驶者的独特驾驶风格。与常规的自适应巡航控制系统相比，该框架能够快速适应新驾驶员并在预测车辆跟驰行为方面具有更高的准确性和安全性。

MetaFollower: 适应性个性化自动车辆跟随

MetaFollower: Adaptable Personalized Autonomous Car Following

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
this https URL

本研究建立了一个公共基准数据集，用于汽车跟车行为建模。数据集包含来自五个公共驾驶数据集的超过 80K 个汽车跟车事件，用于比较不同的跟车模型，并推进更精确模型的发展。实现了代表性的基准模型并进行测试，结果表明，基于 DDPG 模型的跟车模型在大多数数据集中具有竞争力，跟随距离 MSE 值较低，碰撞率较小。