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。