The mixed traffic flow consists of both Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs), with the overall dynamics heavily influenced by the unpredictable driving behavior of HDVs. In this context, we propose a Physics-Informed Data-Enabled Predictive Control (PI-DeePC) method tailored for the optimal control of CAVs. This method seamlessly integrates partial system physics to achieve a balance between safety and traffic efficiency in regulating mixed traffic flow through distributed CAVs. By incorporating system physics described in an input/output mapping function, e.g., a state-space equation, the explorations in the data and physical space can be harmonically achieved in a unified framework to synthesize optimal control decisions. This study encapsulates the physics of mixed traffic flow, incorporating variables such as velocity and acceleration, into a comprehensive state-space equation. Subsequently, we implement the proposed PI-DeePC framework to strategically regulate mixed traffic flows, taking into account the effects of measurement noise and the uncertain, diverse behaviors of HDVs. Simulation results demonstrate the efficacy, reliability, and robustness of the PI-DeePC in controlling CAVs within an unknown environment involving significant noise, as visualized in the enhanced control performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Physics-Informed Data-EnablEd Predictive Control for Regulating Mixed Traffic Flows


    Beteiligte:
    Li, Dongjun (Autor:in) / Dong, Haoxuan (Autor:in) / Song, Ziyou (Autor:in)


    Erscheinungsdatum :

    19.06.2024


    Format / Umfang :

    6279930 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic

    Zhang, Kaixiang / Chen, Kaian / Li, Zhaojian et al. | IEEE | 2024



    CONNECTIVITY-ENABLED TRAFFIC-AWARE SUPPLEMENTAL SENSOR CONTROL FOR INFORMED DRIVING

    KESHAVAMURTHY SHALINI / UCAR SEYHAN / OGUCHI KENTARO | Europäisches Patentamt | 2021

    Freier Zugriff

    Connectivity-enabled traffic-aware supplemental sensor control for informed driving

    KESHAVAMURTHY SHALINI / UCAR SEYHAN / OGUCHI KENTARO | Europäisches Patentamt | 2022

    Freier Zugriff

    Physics-informed Neural Network Predictive Control for Quadruped Locomotion

    Li, Haolin / Chai, Yikang / Lv, Bailin et al. | ArXiv | 2025

    Freier Zugriff