Motion decision-making is an open-challenging issue for autonomous driving, especially in the complex and diverse environment. A motion decision-making model based on deep reinforcement learning (DRL) is proposed in this work. To optimize the driving policy, a multi-objective reward function is designed to guide the autonomous driving system to explore optimal decision policy with the goal of safety, efficiency, and smoothness. A convolutional neural network (CNN) is designed as the backbone network of the DRL model to make full use of the observation information of environment. To enhance safety, a safety check module is designed for avoiding potential unsafe actions. A series of contrast experiments are conducted to justify the model performance of the proposed method on the DeepTraffic simulation environment. The experimental results demonstrate the effectiveness of the proposed DRL-based model in terms of the safe, efficient and smooth motion decision-making, and the generalization ability in different traffic conditions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Autonomous Motion Decision-making based on Deep Reinforcement Learning for Autonomous Driving


    Beteiligte:
    Hu, Jie (Autor:in) / Kong, Huifang (Autor:in) / Liu, Tiankuo (Autor:in) / Meng, Yibo (Autor:in)


    Erscheinungsdatum :

    28.10.2022


    Format / Umfang :

    4826274 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles

    Liu, Teng / Mu, Xingyu / Huang, Bing et al. | ArXiv | 2020

    Freier Zugriff

    Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

    Hoel, Carl-Johan / Driggs-Campbell, Katherine / Wolff, Krister et al. | IEEE | 2020