Stabilizing Traffic via Autonomous Vehicles: A Continuum Mean Field Game Approach
Stabilizing Traffic via Autonomous Vehicles: A Continuum Mean Field Game Approach
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DOI：
10.1109/itsc.2019.8917021
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Kuang Huang;Xuan Di;Q. Du;Xi Chen
中科院分区：
文献类型：
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作者：
Kuang Huang;Xuan Di;Q. Du;Xi Chen
本文提出了基于连续交通流模型的纯自动驾驶车辆 (AV) 交通和混合交通的可扩展交通稳定性分析。人类车辆采用非平衡交通流模型，即 Aw-Rascle-Zhang (ARZ) 进行建模，该模型不稳定。 AV 通过平均场博弈建模，假设 AV 是具有预期能力的理性主体。线性稳定性分析和数值实验表明自动驾驶汽车有助于稳定流量。此外，我们量化了自动驾驶汽车的渗透率和控制器设计对流量稳定性的影响。研究结果可能为自动驾驶汽车制造商和城市规划者提供见解。
This paper presents scalable traffic stability analysis for both pure autonomous vehicle (AV) traffic and mixed traffic based on continuum traffic flow models. Human vehicles are modeled by a non-equilibrium traffic flow model, i.e., Aw-Rascle-Zhang (ARZ), which is unstable. AVs are modeled by the mean field game which assumes AVs are rational agents with anticipation capacities. It is shown from linear stability analysis and numerical experiments that AVs help stabilize the traffic. Further, we quantify the impact of AV’s penetration rate and controller design on the traffic stability. The results may provide insights for AV manufacturers and city planners.