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Consensus-based local information coordination for the networked control of the autonomous intersection management

机译:基于共识的本地信息协调,用于自治路口管理的网络控制

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Autonomous intersection management (AIM) will be a future method for improving traffic efficiency in the urban area. Instead of using the traffic signal control like nowadays, it uses wireless communication with autonomous vehicles to support the management of road traffic more safely and efficiently. A single AIM shows an exceptional performance in managing traffics at an intersection. However, it could not be represented a traffic in the real world, which is composed of multiple intersections. We show that coordination of traffic information among vehicles and infrastructures is an essential part of macroscopic traffic management. Coordination of traffic information among the network of AIMs is the key to improve the overall traffic flow throughout the network not only has an optimal flow in some intersections and very heavy traffic in others. In this paper, we introduce the distributed control to a graph-based intersection network to control traffic in a macroscopic level. Vehicle to infrastructure and infrastructure to infrastructure communication are used to exchange the traffic information between a single autonomous vehicle to the network of autonomous intersections. We implement a discrete time consensus algorithm to coordinate the traffic density of an intersection with its neighborhoods and determine the control policy to maximize a traffic throughput of each intersection as well as stabilizing the overall traffic in the network. We use the Greenshields traffic model to define the boundary condition of various traffic flows to the corresponded traffic density and velocity. Our proposed method represents the ability to maintain traffic flow rate of each intersection without having a back up traffic. As well, every intersection operates under the uncongested flow condition. The simulation results of the graph-based networked control of a multiple autonomous intersection showed that the overall traffic flow in the network achieves up to $$20%$$ 20 % higher than using traffic signal system.
机译:自治路口管理(AIM)将是提高市区交通效率的未来方法。它不再使用当今的交通信号控制,而是使用与自动驾驶车辆的无线通信来更安全,更有效地支持道路交通管理。单个AIM显示出在交叉路口交通管理方面的出色表现。但是,它不能代表由多个路口组成的现实世界中的交通。我们表明,车辆和基础设施之间的交通信息协调是宏观交通管理的重要组成部分。 AIM网络之间交通信息的协调是改善整个网络整体交通流量的关键,不仅在某些交叉路口拥有最佳交通流量,在其他交叉路口拥有非常繁忙的交通流量。在本文中,我们将分布式控制引入到基于图的相交网络中,以在宏观层面上控制交通。车辆到基础设施以及基础设施到基础设施的通信用于在单个自动驾驶车辆与自动交叉路口网络之间交换交通信息。我们实施离散时间共识算法来协调交叉路口及其邻域的交通密度,并确定控制策略以最大化每个交叉路口的交通吞吐量并稳定网络中的总体交通。我们使用Greenshields交通模型将各种交通流的边界条件定义为相应的交通密度和速度。我们提出的方法代表了保持每个十字路口的交通流量而没有备用交通的能力。同样,每个交叉路口在不拥挤的流量条件下运行。基于图形的多自治交叉口网络控制的仿真结果表明,与使用交通信号系统相比,网络中的总体交通流量提高了20%至20%。

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