Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation. Traditional CF models, though simple, often lack generalization capabilities, while many data-driven methods, despite their robustness, operate as "black boxes" with limited interpretability. To bridge this gap, this work introduces a Bayesian Matrix Normal Mixture Regression (MNMR) model that simultaneously captures feature correlations and temporal dynamics inherent in CF behaviors. This approach is distinguished by its separate learning of row and column covariance matrices within the model framework, offering an insightful perspective into the human driver decision-making processes. Through extensive experiments, we assess the model's performance across various historical steps of inputs, predictive steps of outputs, and model complexities. The results consistently demonstrate our model's adeptness in effectively capturing the intricate correlations and temporal dynamics present during CF. A focused case study further illustrates the model's outperforming interpretability of identifying distinct operational conditions through the learned mean and covariance matrices. This not only underlines our model's effectiveness in understanding complex human driving behaviors in CF scenarios but also highlights its potential as a tool for enhancing the interpretability of CF behaviors in traffic simulations and autonomous driving systems.

翻译：学习和理解跟车（CF）行为对于微观交通仿真至关重要。传统CF模型虽然简单，但通常缺乏泛化能力；而许多数据驱动方法尽管具有鲁棒性，却作为“黑箱”运行，可解释性有限。为弥合这一差距，本研究引入了一种贝叶斯矩阵正态混合回归（MNMR）模型，该模型能同时捕捉CF行为中固有的特征相关性与时间动态特性。该方法通过在模型框架内分别学习行协方差矩阵和列协方差矩阵而独具特色，为理解人类驾驶员的决策过程提供了深刻视角。通过大量实验，我们评估了模型在不同历史输入步长、预测输出步长及模型复杂度下的性能。结果一致表明，我们的模型能有效捕捉CF过程中的复杂相关性与时间动态特性。一项聚焦的案例研究进一步展示了模型通过学习得到的均值矩阵和协方差矩阵来识别不同运行状态的可解释性优势。这不仅凸显了模型在理解CF场景中复杂人类驾驶行为方面的有效性，也强调了其作为增强交通仿真和自动驾驶系统中CF行为可解释性工具的潜力。