Path-tracking control is an integral part of motion planning in autonomous vehicles, where a control system on the vehicle will provide acceleration and steering angle commands to ensure accurate tracking of its longitudinal and lateral movements in reference to a pre-defined trajectory. In this paper, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a deep reinforcement learning (DRL) model is developed to facilitate the control of the vehicle's longitudinal speed. A deep deterministic policy gradient algorithm is employed to train the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed to control the steering angle of the vehicle with the main goal of following a reference trajectory. Finally, the longitudinal and lateral control models are coupled to obtain a complete path-tracking controller at a wide range of vehicle speeds. The state-of-the-art model-based path-tracking controller is also built (using the model predictive control and Stanley control) to evaluate the performance of the proposed model. The results showed that the performance of the proposed data-driven DRL control model is effective compared with model-based control approaches (in terms of the velocity error, lateral yaw angle error, and lateral distance error).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Data-driven Deep Reinforcement Learning for Automated Driving


    Beteiligte:
    Prabu, Avinash (Autor:in) / Li, Lingxi (Autor:in) / Chen, Yaobin (Autor:in) / King, Brian (Autor:in)


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    4580923 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Automated eco-driving in urban scenarios using deep reinforcement learning

    Wegener, Marius / Koch, Lucas / Eisenbarth, Markus et al. | Elsevier | 2021



    Distributional Reinforcement Learning For Automated Driving Vehicle

    Naveen, A M / Ravish, Roopa / Ranga Swamy, Shanta | IEEE | 2022


    Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving

    Yurtsever, Ekim / Capito, Linda / Redmill, Keith et al. | IEEE | 2020


    Autonomous Driving with Deep Reinforcement Learning

    Zhu, Yuhua / Technische Universität Dresden | SLUB | 2023