This paper presents a predictive controller for longitudinal motion of automated vehicles based on Deep Reinforcement Learning. It uses advance information about future speed reference values and road grade changes. The incorporation of this information leads to a new design parameter with a high influence on learning speed: the selection of proper advance knowledge signals during training. We propose a design method which shows improved learning performance in our experiments. The performance of our controller is explored through simulation of a real world driving scenario in a parking garage. We demonstrate that our Reinforcement Learning agent can learn a behavior close to the optimal solution of a Nonlinear Model Predictive Controller, but at reduced computational costs.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Reinforcement Learning for Predictive Longitudinal Control of Automated Vehicles


    Beteiligte:
    Bucchel, Martin (Autor:in) / Knoll, Alois (Autor:in)


    Erscheinungsdatum :

    01.11.2018


    Format / Umfang :

    1477485 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep reinforcement learning-based longitudinal control strategy for automated vehicles at signalised intersections

    Kumar, Pankaj / Mishra, Aditya / Chakraborty, Pranamesh et al. | ArXiv | 2025

    Freier Zugriff



    Reinforcement Learning-Based Predictive Control for Autonomous Electrified Vehicles

    Liu, Teng / Yang, Chao / Hu, Chuanzheng et al. | IEEE | 2018


    REINFORCEMENT LEARNING-BASED PREDICTIVE CONTROL FOR AUTONOMOUS ELECTRIFIED VEHICLES

    Liu, Teng / Yang, Chao / Hu, Chuanzheng et al. | British Library Conference Proceedings | 2018