The invention relates to a non-signal-control intersection networked vehicle ecological driving method based on deep reinforcement learning. The method comprises the following steps that 1) a deep reinforcement learning algorithm is coupled with a microscopic traffic flow model to drive a strategy module to generate traffic flow features; the vehicle cooperative control collision avoidance strategy module adjusts the speed and path of the vehicle; the ecological driving strategy module calculates instantaneous energy consumption and carbon emission of the vehicle; 2) through input and feedback of the three modules, a deep reinforcement learning coupling model cooperative control module obtains three function modules of ecological energy saving, high efficiency and safety and driving comfort award; 3) forming a multi-stage multi-target reward function module; 4) the traffic scene module simulates a real traffic environment; and 5) calling data of the multi-stage multi-target reward function module and the traffic scene module by the main function module, and finally outputting a non-signal-control intersection automobile automatic driving strategy. Through the synergistic effect of all the modules, self-adaptive ecological driving can be realized in a complex dynamic traffic environment.

    本发明涉及一种非信控路口基于深度强化学习的网联汽车生态驾驶方法,包括如下步骤:1),基于深度强化学习算法耦合微观交通流模型驱动策略模块生成交通流特征;车辆协同控制避免碰撞策略模块调整车辆速度和路径;生态驾驶策略模块计算车辆瞬间能耗和碳排放;2),通过以上三个模块的输入和反馈,深度强化学习耦合模型协同控制模块获得生态节能、高效安全和驾驶舒适奖励三个函数模块;3),构成多阶段多目标奖励函数模块;4),交通场景模块模拟真实交通环境;5),主函数模块调取多阶段多目标奖励函数模块和交通场景模块数据,最终输出非信控路口汽车自动驾策略。本发明通过各模块的协同作用,能够在复杂动态交通环境下实现自适应生态驾驶。


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Networked automobile ecological driving method based on deep reinforcement learning at non-signal-control intersection


    Weitere Titelangaben:

    非信控路口基于深度强化学习的网联汽车生态驾驶方法


    Beteiligte:
    WANG WEIYUE (Autor:in) / WEI XINKAI (Autor:in) / SONG TAO (Autor:in) / TAN CHANGLIANG (Autor:in) / WANG WEI (Autor:in) / KIM SUK-YEONG (Autor:in) / SHEN RUOYUN (Autor:in) / WANG YANLIN (Autor:in) / YANG KEFAN (Autor:in) / WEI YANFANG (Autor:in)

    Erscheinungsdatum :

    14.03.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



    Urban intersection signal control method based on deep reinforcement learning

    JU YANNI / MA DIE | Europäisches Patentamt | 2024

    Freier Zugriff

    Method for optimizing driving path of intelligent networked automobile at intersection without signal lamp

    ZHANG ZHI / TAN GUOPING / BAO XIYAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Multi-intersection signal cooperative control method based on deep reinforcement learning

    MA CHANGXI / LIU YAN / ZHAO HONGXING et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    Single-intersection signal control method based on deep reinforcement learning algorithm

    HUANG YIWANG / WU QIAN | Europäisches Patentamt | 2023

    Freier Zugriff

    Multi-intersection traffic signal control method based on deep reinforcement learning

    LIU LIJUAN / BAI GUANGMING | Europäisches Patentamt | 2023

    Freier Zugriff