The advancement of connected and automated trucks (CATs) offers a novel avenue for the freight industry to optimize fuel efficiency, enhance traffic flow, and bolster safety through platooning. Particularly at highway on-ramps, how to effectively form CAT platoons is a key research topic. Previous studies have not addressed the issue of platooning at on-ramps. Within mixed traffic conditions, the interference from human driven vehicle (HDV) notably complicates the platooning operation. In this process, the position, timing, and speed of CAT merging significantly affect energy consumption and traffic safety. Thus, this work introduces a hierarchical merging strategy with a dynamic mechanism for selecting optimal merging points. Its objective is to facilitate efficient autonomous CAT platooning at highway on-ramps while considering the interference of HDVs. Specifically, it uses a model-free deep reinforcement learning method that guides the platooning process by exploring optimal driving behaviors. It ensures the safety and efficiency of the CAT merging process. Furthermore, we incorporate a realistic vehicle dynamics model within our simulations. The proposed strategy can handle the variation of the CATs’ initial positions and speeds at on-ramps, as well as interference caused by HDVs at highway mainline. The efficacy of the proposed strategy has been verified through a series of simulation experiments. The findings indicate that our strategy is capable of effectively orchestrating CAT platooning maneuvers at the highway on-ramps.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Highway On-Ramp Truck Platooning Based on Deep Reinforcement Learning


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Wang, An (Autor:in) / Qi, Liang (Autor:in) / Luan, Wenjing (Autor:in) / Lu, Tong (Autor:in)


    Erscheinungsdatum :

    12.12.2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A Highway Entrance Ramp Control Method Based on Deep Reinforcement Learning

    Wu, Lin / Li, Zhenlong / Diao, Mengxiao et al. | IEEE | 2023


    Truck Platooning Application

    Ellwanger, Simon / Wohlfarth, Enrico | British Library Conference Proceedings | 2017


    Benefits and Risks of Truck Platooning on Freeway Operations Near Entrance Ramp

    Wang, Meng / van Maarseveen, Sander / Happee, Riender et al. | Transportation Research Record | 2019


    Truck Platooning Worldwide

    Atasayar, Hatun / Blass, Philipp / Kaiser, Susanne | Springer Verlag | 2022

    Freier Zugriff