With the increasing number of vehicles, the traffic congestion is becoming more and more serious. In order to alleviate such a problem, this article considers transmission and inference delay of cloud centralized computing in the software defined Internet of Vehicles (SDIoV), and builds a new SDIoV architecture based on edge intelligence, for supporting real-time vehicle routing decision through distributed multi-agent reinforcement learning model. Then, a software defined device collaboration optimization method is designed to improve the efficiency of distributed training. Combined with multi-agent reinforcement learning, a distributed-learning-based vehicle routing decision algorithm (DLRD) is proposed to adaptively adjust vehicle routing online. The performed simulations show that the DLRD can successfully realize real-time routing decision for vehicles and alleviate traffic congestion with the dynamic changes of the road environment.


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    Titel :

    Distributed Learning for Vehicle Routing Decision in Software Defined Internet of Vehicles


    Beteiligte:
    Lin, Kai (Autor:in) / Li, Chensi (Autor:in) / Li, Yihui (Autor:in) / Savaglio, Claudio (Autor:in) / Fortino, Giancarlo (Autor:in)


    Erscheinungsdatum :

    01.06.2021


    Format / Umfang :

    4856707 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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