发表机构
Vanderbilt University（范德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种通过直接施加无增长特征模态来降低保守性的方法，以最少自动驾驶车辆数量稳定异构交通流，并通过非线性优化求解控制参数，数值模拟显示AV渗透率可显著降低。
AI 中文摘要
本文探讨了在控制约束下使用最少数量的自动驾驶车辆（AVs）来稳定交通流。与大多数研究不同，我们考虑了人类驾驶车辆（HVs）的异构参数设置场景，以反映真实世界中驾驶行为的差异。虽然现有文献使用基于H-Infinity的充分条件来确保交通流的弦稳定性，但这通常会产生一个保守的AV渗透率下界来稳定交通流。为减少这种保守性并获得更不保守的下界，我们通过直接施加无增长特征模态来确保交通流的弦稳定性。我们还系统地找到了所需AV的最小数量，并通过非线性优化求解最优控制参数。最后，我们通过数值模拟评估了预期的保守性降低效果。定量地，将我们的算法应用于文献中的同质HV基线（基于H-Infinity充分条件的结果），在确保交通流弦稳定性的同时，将AV渗透率降低了（改善了）17.14%。我们观察到稳定化/性能退化与所用相同AV数量之间存在权衡。定量地，我们的最后一次数值模拟证实，在确保交通流弦稳定性的同时，AV渗透率可以降低61.54%，但代价是位置偏差相对于平衡态高出27.66%，以及与聚合线性化动力学相关的实际稳定半径（RSR）——衡量扰动/不确定性下稳定鲁棒性的指标——退化92.47%。这种权衡有助于工程师/操作员做出更好的交通控制决策。
英文摘要
This paper explores stabilizing traffic flow using a minimum number of autonomous vehicles (AVs) under control constraints. In contrast to most studies, we consider a heterogeneous parameter setup scenario for human-driven vehicles (HVs) to reflect real-world differences in driving behavior. While current literature uses an H-Infinity based sufficient condition to ensure the string stability of traffic flow, this often yields a conservative lower bound on the AV penetration rate to stabilize traffic flow. To reduce such conservativeness and obtain a less conservative lower bound, we ensure the string stability of traffic flow by directly imposing the possession of no growing eigenmodes. We also systematically find a minimum number of required AVs and solve for the optimal control parameters via nonlinear optimization. We finally assess the intended conservativeness reduction via numerical simulations. Quantitatively, applying our algorithm to the homogeneous HV baseline in the literature (the result built upon an H-Infinity based sufficient condition) reduces (improves) the AV penetration rate by 17.14% while ensuring the string stability of traffic flow. We observe a trade-off between the stabilization/performance degradation and the number of utilized identical AVs. Quantitatively, our last numerical simulation corroborates that the AV penetration rate can be reduced by 61.54% at the expense of 27.66% higher position difference deviation from the equilibrium and a 92.47% degradation in the real stability radius (RSR)---a metric to measure the stability robustness under the perturbation/uncertainty---associated with the aggregated linearized dynamics while ensuring the string stability of traffic flow. This trade-off helps engineers/operators make better traffic control decisions.
