We propose a fully distributed control system architecture, amenable to in-vehicle implementation, that aims to safely coordinate connected and automated vehicles (CAVs) at road intersections. For control purposes, we build upon a fully distributed model predictive control approach, in which the agents solve a nonconvex optimal control problem (OCP) locally and synchronously, and exchange their optimized trajectories via vehicle-to-vehicle (V2V) communication. To accommodate a fast solution of the nonconvex OCPs, we apply the penalty convex-concave procedure which solves a convexified version of the original OCP. For experimental evaluation, we complement the predictive controller with a localization layer, being in charge of self-localization, and an estimator, which determines joint collision points with other agents. Experimental tests reveal the efficacy of the proposed control system architecture.

中文翻译：

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交叉口联网自动车辆的全分布式模型预测控制：理论和车辆实验


我们提出了一种适合车载实施的完全分布式控制系统架构，旨在安全地协调道路交叉口的联网和自动驾驶车辆（CAV）。出于控制目的，我们建立了完全分布式模型预测控制方法，其中代理在本地同步解决非凸最优控制问题（OCP），并通过车辆对车辆（V2V）通信交换其优化轨迹。为了适应非凸 OCP 的快速解决方案，我们应用惩罚凸凹过程来求解原始 OCP 的凸化版本。对于实验评估，我们用负责自定位的定位层和确定与其他代理的联合碰撞点的估计器来补充预测控制器。实验测试揭示了所提出的控制系统架构的有效性。