通过车路协同实现高速匝道汇入的分布式计算与控制，提升效率且保障安全。
V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario
- 利用车路通信将全局优化任务分摊到各车辆并行求解，摆脱中心控制器依赖。
- 在严格安全约束下，实现高维非凸多车模型预测控制，收敛速度快于传统方法。
- 适合智能网联汽车系统设计者及交通控制研究者参考，尤其关注实时性与安全性场景。
本文研究在交通信息物理系统下，连通自动驾驶车辆（CAVs）在匝道汇入场景中的分布式计算与协同控制问题。首先，针对匝道汇入的安全约束与交通性能，建立集中式协同轨迹规划问题，对所有车辆轨迹进行联合优化。为摆脱对中心控制器的依赖并降低计算时间，提出一种基于车路协同（V2X）通信的分布式求解方法，可将计算任务分发至各车辆并行处理。其次，基于上述解，构建以最大化系统稳定性、最小化控制输入为目标的多车模型预测控制（MPC）问题，受严格安全约束和输入限值限制，该问题具有高维、集中式、非凸特性。为此，提出分解与凸化方法——分布式协同迭代模型预测控制（DCIMPC），利用车辆间通信能力分解问题，充分调动车载计算资源，实现快速求解与分布式控制。两个问题及其求解方法共同构成车路协同的分布式计算与控制框架。仿真验证了该框架的收敛性、安全性与求解速度；额外实验进一步验证了DCIMPC性能。结果表明，本方法在不牺牲系统性能的前提下显著提升计算速度。
原文摘要 · Abstract (English)
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.
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