On multi-class automated vehicles: Car-following behavior and its implications for traffic dynamics
On multi-class automated vehicles: Car-following behavior and its implications for traffic dynamics
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DOI：
10.1016/j.trc.2021.103166
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Wissam Kontar;Tienan Li;A. Srivastava;Yang Zhou;Danjue Chen;Soyoung Ahn
中科院分区：
文献类型：
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作者：
Wissam Kontar;Tienan Li;A. Srivastava;Yang Zhou;Danjue Chen;Soyoung Ahn
提出了一个统一的框架来揭示自动车辆在不同控制模式和参数设置下的物理跟车行为。该框架采用灵活的非对称行为(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。然后得到AB模型参数和控制参数之间的映射关系，以了解CF行为的可能范围。最后，设计了一种基于Logistic分类器和卷积多元高斯过程(MGP)的预测建模方法来预测AV的CF行为。通过对两种著名控制器--线性状态反馈控制器和模型预测控制(MPC)的分析，说明了所提出的模型预测控制框架能够揭示系统的状态反馈机制，并对交通级扰动的演化提供了深入的了解。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，它可以指导音响控制设计，不是短视的，而是考虑交通级别的性能。
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 (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.