This paper presents a Deep Reinforcement Learning (DRL) framework adapted and trained for Autonomous Vehicles (AVs) purposes. To do that, we propose a novel software architecture for training and validating DRL based control algorithms that exploits the concepts of standard communication in robotics using the Robot Operating System (ROS), the Docker approach to provide the system with portability, isolation and flexibility, and CARLA (CAR Learning to Act) as our hyper-realistic open-source simulation platform. First, the algorithm is introduced in the context of Self-Driving and DRL tasks. Second, we highlight the steps to merge the proposed algorithm with ROS, Docker and the CARLA simulator, as well as how the training stage is carried out to generate our own model, specifically designed for the AV paradigm. Finally, regarding our proposed validation architecture, the paper compares the trained model with other state-of-the-art traditional control approaches, demonstrating the full strength of our DL based control algorithm, as a preliminary stage before implementing it in our real-world autonomous electric car.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Reinforcement Learning based control algorithms: Training and validation using the ROS Framework in CARLA Simulator for Self-Driving applications


    Beteiligte:


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    7364130 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    DEEP REINFORCEMENT LEARNING BASED CONTROL ALGORITHMS: TRAINING AND VALIDATION USING THE ROS FRAMEWORK IN CARLA SIMULATOR FOR SELF-DRIVING APPLICATIONS

    Pérez-Gill, óscar / Barea, Rafael / López-Guillén, Elena et al. | British Library Conference Proceedings | 2021


    Autonomous Navigation with Deep Reinforcement Learning in Carla Simulator

    Wang, Peilin / Technische Universität Dresden | SLUB | 2023



    Validation Method of a Self-Driving Architecture for Unexpected Pedestrian Scenario in CARLA Simulator

    Gutierrez, Rodrigo / Arango, J. Felipe / Gomez-Huelamo, Carlos et al. | IEEE | 2021


    VALIDATION METHOD OF A SELF-DRIVING ARCHITECTURE FOR UNEXPECTED PEDESTRIAN SCENARIO IN CARLA SIMULATOR

    Gutiérrez, Rodrigo / Arango, J. Felipe / Gomez-Huélamo, Carlos et al. | British Library Conference Proceedings | 2021