辅助驾驶车辆对一般二阶交通模型的多尺度控制
Multiscale control of generic second order traffic models by driver-assist vehicles
摘要：
我们研究了通用高阶宏观交通模型的推导，通过动力学方法从跟随领导者的粒子描述。首先，我们恢复了一个三阶交通模型的Enskog型动力学方程的流体动力学极限。接下来，我们在车辆的相互作用中引入了一个二元控制模型，该模型由驾驶辅助车辆提供的自动反馈，并且我们通过另一个基于Enskog的流体动力学限制来放大这样一个新的粒子描述。由此产生的宏观模型现在是一个通用二阶模型（GSOM），它又包含一个控制项继承自微观相互作用。我们表明，这样的控制可以选择，以优化全球交通趋势，如车辆流量或道路拥堵，受GSOM动态。通过数值模拟，我们研究了这种控制层次结构在一些特定的案例研究，这是从车辆智能实施的驾驶员辅助控制的多尺度路径，其最佳流体动力学设计的效果。
英文摘要：
We study the derivation of generic high order macroscopic traffic models from a follow-the-leader particle description via a kinetic approach. First, we recover a third order traffic model as the hydrodynamic limit of an Enskog-type kinetic equation. Next, we introduce in the vehicle interactions a binary control modelling the automatic feedback provided by driver-assist vehicles and we upscale such a new particle description by means of another Enskog-based hydrodynamic limit. The resulting macroscopic model is now a Generic Second Order Model (GSOM), which contains in turn a control term inherited from the microscopic interactions. We show that such a control may be chosen so as to optimise global traffic trends, such as the vehicle flux or the road congestion, constrained by the GSOM dynamics. By means of numerical simulations, we investigate the effect of this control hierarchy in some specific case studies, which exemplify the multiscale path from the vehicle-wise implementation of a driver-assist control to its optimal hydrodynamic design.
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