This paper develops a unifying framework to unveil the physical car-following (CF) behaviors of automated vehicles (AVs) under different control paradigms and parameter settings. The proposed framework adopts the flexible asymmetric behavior (AB) model to reveal the control mechanisms and their manifestation in the physical CF behavior, particularly their response to traffic disturbances. A mapping relationship between the AB model parameters and control parameters is then obtained to understand the range of CF behavior possible. Finally, a predictive modeling approach based on a logistic classifier coupled with a convoluted Multivariate Gaussian Process ($\mathcal{MGP}$) is designed to predict the CF behavior of an AV. Analysis of two well-known controllers, linear state-feedback and Model Predictive Control (MPC), show how the proposed framework can uncover the CF mechanisms and provide insights into traffic-level disturbance evolution. The proposed analysis framework remains scalable and can be applied to a variety of controllers. Ultimately, it can guide AV control design that is not myopic, but considers traffic-level performance.

本文开发了一个统一的框架，以揭示在不同的控制范式和参数设置下自动驾驶汽车（AV）的物理跟车（CF）行为。所提出的框架采用了灵活的不对称行为（AB）模型来揭示控制机制及其在物理CF行为中的表现，特别是它们对交通干扰的响应。然后获得AB模型参数和控制参数之间的映射关系，以了解可能的CF行为范围。最后，基于逻辑分类器和卷积多元高斯过程（$\mathcal{MGP}$）旨在预测AV的CF行为。对两个著名的控制器（线性状态反馈和模型预测控制（MPC））的分析表明，所提出的框架如何能够揭示CF机制并提供对流量级扰动演变的见解。所提出的分析框架保持可伸缩性，并且可以应用于各种控制器。最终，它可以指导非近视的AV控制设计，但要考虑流量级别的性能。