This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized. To get rid of the reliance on a central controller and reduce computation time, a distributed solution to this problem implemented among CAVs through Vehicles-to-Everything (V2X) communication is proposed. Unlike existing method, our method can distribute the computational task among CAVs and carry out parallel solving through V2X communication. Then, a multi-vehicles model predictive control (MPC) problem aimed at maximizing system stability and minimizing control input is formulated based on the solution of the first problem subject to strict safety constants and input limits. Due to these complex constraints, this problem becomes high-dimensional, centralized, and non-convex. To solve it in a short time, a decomposition and convex reformulation method, namely distributed cooperative iterative model predictive control (DCIMPC), is proposed. This method leverages the communication capability of CAVs to decompose the problem, making full use of the computational resources on vehicles to achieve fast solutions and distributed control. The two above problems with their corresponding solving methods form the systemic framework of the V2X assisted distributed computing and control. Simulations have been conducted to evaluate the framework's convergence, safety, and solving speed. Additionally, extra experiments are conducted to validate the performance of DCIMPC. The results show that our method can greatly improve computation speed without sacrificing system performance.

翻译：本文研究了交通信息物理系统中匝道汇入场景下网联自动驾驶车辆的分布式计算与协同控制问题。首先，在考虑匝道汇入场景安全约束与交通性能的前提下，建立了集中式协同轨迹规划问题，该问题对所有车辆的轨迹进行联合优化。为消除对中央控制器的依赖并减少计算时间，提出了一种通过车联网通信在网联自动驾驶车辆间实现的分布式解决方案。与现有方法不同，本方法能够将计算任务分配至各网联自动驾驶车辆，并通过车联网通信进行并行求解。随后，基于首个问题的解，在严格安全约束与控制输入限制条件下，构建了以最大化系统稳定性和最小化控制输入为目标的多车辆模型预测控制问题。由于这些复杂约束，该问题呈现高维、集中式和非凸特性。为实现快速求解，提出了一种分解与凸化重构方法，即分布式协同迭代模型预测控制。该方法利用网联自动驾驶车辆的通信能力对问题进行分解，充分运用车载计算资源实现快速求解与分布式控制。上述两个问题及其对应求解方法共同构成了V2X辅助的分布式计算与控制体系框架。通过仿真实验评估了该框架的收敛性、安全性与求解速度。此外，通过额外实验验证了分布式协同迭代模型预测控制的性能。结果表明，本方法能在不牺牲系统性能的前提下显著提升计算速度。