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首页> 外文期刊>Computer-Aided Civil and Infrastructure Engineering >Graph neural network and reinforcement learning for multi-agent cooperative control of connected autonomous vehicles
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Graph neural network and reinforcement learning for multi-agent cooperative control of connected autonomous vehicles

机译：图形神经网络和加固学习，用于连接自动车辆多功能协作控制

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摘要

A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high-level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast-growing joint action space associated with multi-agent driving tasks pose difficultly in achieving cooperative control. The problem is NP-hard and cannot be efficiently resolved using rule-based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning-based algorithm that combines graphic convolution neural network with deep Q-network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi-lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane-changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis-a-vis traditional rule-based and long short-term memory-based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane-change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.
机译：连接的自主车辆（CAV）网络可以被定义为一组连接的车辆，包括在可以是道路网络，走廊或段的特定空间范围上操作的脉冲。空间范围构成了共享交通信息的环境，并发出指令以控制骑士船只运动。在这种空间范围内，通过联合规划和控制流动培养的骑士的高级别合作可以大大提高其运营的安全性和移动性能。遗憾的是，由于动态的代理（车辆）和与多售后驾驶任务相关联的快速增长的接合动作空间难以实现协作控制，这是CAV网络的高度组合和挥发性，并且在实现协作控制方面困难。问题是NP - 硬，无法使用基于规则的控制技术有效地解决。此外，关于CAV操作中的传感技术和控制逻辑的文献中存在大量信息，但是关于来自协作感测和连接源的信息集成的信息相对较少。因此，我们提出了一种基于新型的深度加强学习基于学习的算法，将图形卷积神经网络与Deep Q-Network结合，形成了一种用于信息融合模块和决策处理器的创新图形卷积Q网络。在这项研究中，我们考虑的Sac网络是一个多车道道路走廊。我们展示了使用高速公路车道改变的申请背景下提出的控制算法在出口坡道的方法中。出于比较的目的，评估了基于传统规则的基于规则和长短期内存的融合模型的拟议模型。结果表明，所提出的模型能够以增强安全性和移动性的方式分焦从感测和连接源接收的信息，并规定了多个骑士的有效操作道德决策。这样，即使在部分观察到的和高度动态的混合交通流量中，也可以满足各个脉冲的操作意图。本文提出了实验证据证明该算法可以显着增强CAV操作。该算法可以在路边单元或云平台或其他集中控制设施中部署。

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    Chen Sikai; Dong Jiqian; Ha Paul (Young Joun); Li Yujie; Labi Samuel;

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    Purdue Univ Ctr Connected & Automated Transportat CCAT W Lafayette IN 47907 USA|Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA|Carnegie Mellon Univ Sch Comp Sci Robot Inst Pittsburgh PA 15213 USA;

    Purdue Univ Ctr Connected & Automated Transportat CCAT W Lafayette IN 47907 USA|Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA;

    Purdue Univ Ctr Connected & Automated Transportat CCAT W Lafayette IN 47907 USA|Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA;

    Purdue Univ Ctr Connected & Automated Transportat CCAT W Lafayette IN 47907 USA|Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA;

    Purdue Univ Ctr Connected & Automated Transportat CCAT W Lafayette IN 47907 USA|Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA;

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