Synchronous and asynchronous parallel computation for large-scale optimal control of connected vehicles

Abstract

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.