In this paper, we propose a data-driven predictive controller for connected automated vehicles (CAVs) traveling in mixed traffic consisting of both connected and non-connected vehicles. We assume a low penetration of connectivity, with only one connected vehicle in the downstream traffic. A model predictive controller is designed to integrate multiple specifications, including safety and energy efficiency, while accounting for the time delay in the longitudinal dynamics of the vehicle. A data-driven prediction method based on the behavioral theory of linear systems is proposed to model the relationship between the speeds of the distant connected vehicle and the vehicle immediately in front of the CAV. The proposed method is evaluated using real traffic data and demonstrates improved prediction accuracy and energy efficiency compared to model-based prediction methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Data-driven Predictive Connected Cruise Control


    Beteiligte:
    Shen, Minghao (Autor:in) / Orosz, Gabor (Autor:in)


    Erscheinungsdatum :

    04.06.2023


    Format / Umfang :

    9961124 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Energy-Efficient Reactive and Predictive Connected Cruise Control

    Shen, Minghao / Dollar, Robert Austin / Molnar, Tamas G. et al. | IEEE | 2024


    Predictive Cruise Control of Connected Vehicle With Online Parameters Learning

    Wang, Yuhao / Gong, Xun / Lin, Jiamei et al. | IEEE | 2021



    An Enhanced Predictive Cruise Control System Design With Data-Driven Traffic Prediction

    Jia, Dongyao / Chen, Haibo / Zheng, Zuduo et al. | IEEE | 2022


    Robust Design of Connected Cruise Control Among Human-Driven Vehicles

    Hajdu, David / Ge, Jin I. / Insperger, Tamas et al. | IEEE | 2020