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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    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





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

    Li, Shengbo Eben / Wang, Zhitao / Zheng, Yang et al. | Elsevier | 2020


    Connected Vehicles and Driving Automation Systems

    Nakanishi, Yuko J. / Auza, Pierre M. | Springer Verlag | 2023


    Distributed model predictive control methods for intermodal transport cooperative planning based on ADMM

    Zhou, Qicai / Huang, Yuankai / Xiong, Xiaolei et al. | Wiley | 2023

    Free access