Connected vehicles is an important intelligent transportation system to improve the traffic performance. This paper proposes two parallel computation algorithms to solve a large-scale optimal control problem in the coordination of multiple connected vehicles. The coordination is formulated as a centralized optimization problem in the receding horizon fashion. A decentralized computation network is designed to facilitate the development of parallel algorithms. We use Taylor series to linearize non-convex constraints, and introduce a set of consensus constraints to transform the centralized problem to a standard consensus optimization problem. A synchronous parallel algorithm is firstly proposed to solve the consensus optimization problem by applying the alternating direction method of multipliers (ADMM). The ADMM framework allows us to decompose the coupling constraints and decision variables, leading to parallel iterations for each vehicle in a synchronous fashion. We then propose an asynchronous version of the parallel algorithm that allows the vehicles to update their variables asynchronously in the computation network. The effectiveness and efficiency of the proposed algorithms are validated by extensive numerical simulations.

联网车辆是提高交通性能的重要智能交通系统。提出了两种并行计算算法，以解决多连接车辆协调中的大规模最优控制问题。协调以后退的方式被公式化为集中优化问题。分散计算网络旨在促进并行算法的开发。我们使用泰勒级数线性化非凸约束，并引入一组共识约束将集中式问题转换为标准共识优化问题。首先提出了一种同步并行算法，通过乘子的交替方向法解决共识优化问题。（ADMM）。ADMM框架允许我们分解耦合约束和决策变量，从而以同步方式导致每辆车的并行迭代。然后，我们提出了并行算法的异步版本，该版本允许车辆在计算网络中异步更新其变量。大量的数值模拟验证了所提算法的有效性和效率。