  This paper proposes a parallel optimization algorithm for cooperative
automation of large-scale connected vehicles. The task of cooperative
automation is formulated as a centralized optimization problem taking the whole
decision space of all vehicles into account. Considering the uncertainty of the
environment, the problem is solved in a receding horizon fashion. Then, we
employ the alternating direction method of multipliers (ADMM) to solve the
centralized optimization in a parallel way, which scales more favorably to
large-scale instances. Also, Taylor series is used to linearize nonconvex
constraints caused by coupling collision avoidance constraints among
interactive vehicles. Simulations with two typical traffic scenes for multiple
vehicles demonstrate the effectiveness and efficiency of our method.
