Deep reinforcement learning has shown potential in autonomous driving decision-making. However, vehicle decision-making involves complex information, and limited state information often limits the ability of agents to make optimal decisions. We present a novel on-ramp decision-making method using the SAC (Soft Actor-Critic) algorithm, which integrates the driving intentions of surrounding vehicles. Our model captures the vehicle characteristics of the target lane and its adjacent lanes as the state space. Additionally, we develop a hybrid action space that combines discrete lateral actions with continuous longitudinal actions, enabling the agent to adapt more effectively to intricate driving scenarios. The efficacy of our approach is validated through simulations using SUMO (Simulation of Urban MObility) and real-world road datasets. Comparative analysis of experimental results illustrates that our model surpasses alternative approaches in terms of collision rate and success rate. Moreover, the model exhibits a stable success rate under various road traffic density conditions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning with Driving Intention for On-Ramp Decision-Making


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liu, Lianqing (editor) / Niu, Yifeng (editor) / Fu, Wenxing (editor) / Qu, Yi (editor) / Fang, Huazhen (author) / Liu, Li (author) / Gu, Qing (author) / Xiao, Xiaofeng (author) / Meng, Yu (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2024 ; Shenyang, China September 19, 2024 - September 21, 2024



    Publication date :

    2025-04-12


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Intelligent automobile decision-making method based on driving intention and deep reinforcement learning

    PEI XIAOFEI / LU SONGXIN / YANG BO | European Patent Office | 2024

    Free access

    Deep reinforcement learning algorithm based ramp merging decision model

    Chen, Zeyu / Du, Yu / Jiang, Anni et al. | SAGE Publications | 2025



    Automatic driving decision-making method based on deep reinforcement learning

    LIU CHENGQI / LIU SHAOWEIHUA / ZHANG YUJIE et al. | European Patent Office | 2024

    Free access

    Automatic driving decision-making system based on deep reinforcement learning

    ZHENG XIAOYAO / YAO QINGHE / ZHANG JIANPENG et al. | European Patent Office | 2024

    Free access