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.


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    Title :

    Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM


    Contributors:
    Wang, Zhitao (author) / Zheng, Yang (author) / Li, Shengbo Eben (author) / You, Keyou (author) / Li, Keqiang (author)


    Publication date :

    2018-11-01


    Size :

    606881 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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