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.

翻译：跟驰（CF）建模作为微观交通仿真的基础组成部分，在过去数十年间日益受到研究者的关注。本研究提出一种基于元学习能力的可适应个性化跟驰框架——MetaFollower。具体而言，我们首先利用模型无关元学习（MAML）从多样化的跟驰事件中提取共性驾驶知识；随后，预训练模型仅需少量新驾驶员的跟驰轨迹即可进行微调，实现个性化跟驰适应。我们进一步结合长短期记忆网络（LSTM）与智能驾驶员模型（IDM），在保持高可解释性的同时反映时间异质性。与传统自适应巡航控制（ACC）系统依赖预设参数且未考虑异质驾驶特性不同，MetaFollower能够精准捕捉并模拟跟驰行为的复杂动力学特征，同时兼顾个体驾驶员的独特驾驶风格。通过展示其在有限训练数据下快速适应新驾驶员的能力，我们验证了MetaFollower的多功能性与适应性。为评估模型性能，我们开展了严谨的实验，将其与数据驱动模型及物理模型进行对比。结果表明，所提框架在跟驰行为预测方面以更高精度和安全性超越基线模型。据我们所知，这是首个基于元学习技术、兼顾驾驶员异质性与时间异质性以实现快速适应的跟驰模型。