The invention relates to the technical field of intelligent traffic, in particular to an intelligent networked PHEV multi-driving-mode autonomous decision control method based on deep learning. The method comprises the steps of designing a state space and an action space based on a deterministic strategy gradient algorithm by constructing three driving modes of a cruise scene, a car-following scene and a passing scene; a multi-target award function is constructed for driving safety, riding comfort and energy consumption economy, a Markov decision process is utilized to construct a multi-target driving mode autonomous decision control model, and it is ensured that when the intelligent network connection PHEV is driven in a complex environment, the optimal target vehicle speed can be planned by autonomously deciding different driving modes. The comprehensive optimization of safety, trafficability and energy consumption economy of the intelligent networked PHEV in the driving process is realized, and the decision-making ability of the intelligent vehicle in a complex traffic environment is further improved.
本发明涉及智能交通技术领域,具体涉及一种基于深度学习的智能网联PHEV多行驶模式自主决策控制方法:通过构建巡航场景、跟驰场景和通行场景三种行驶模式,设计基于确定性策略梯度算法的状态空间与动作空间,并针对行车安全性、乘车舒适性和能耗经济构建多目标奖励函数,利用马尔可夫决策过程搭建多目标行驶模式自主决策控制模型,保证智能网联PHEV在复杂环境下行驶时,可以通过自主决策不同的行驶模式从而规划出最优目标车辆速度,实现智能网联PHEV在行驶过程中安全性、通行性以及能耗经济性综合最优,进一步提高智能车辆在复杂交通环境中的决策能力。
Intelligent networked PHEV multi-driving-mode autonomous decision control method based on deep learning
一种基于深度学习的智能网联PHEV多行驶模式自主决策控制方法
2025-02-28
Patent
Electronic Resource
Chinese
IPC: | 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 |
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