The invention discloses an automatic driving vehicle decision-making method based on implicit time scale reinforcement learning. The method comprises a decision-making model based on implicit time scales, an improved reinforcement learning exploration method, a moving method based on rules and learning and an intelligent environment vehicle model. Time scale information is imported into a state value table by an implicit method through a decision model based on implicit time scales, so that the continuity of decision behaviors is improved; according to the improved reinforcement learning exploration method, an exploration method based on a preset strategy, a value table updating method based on back propagation and a local optimal point breakthrough based on a momentum method are used for improving the exploration efficiency of reinforcement learning; the moving method based on rules and learning and the intelligent environment vehicle model can establish a simulation environment to verify the effectiveness and continuity of the decision. According to the invention, the time information is hidden in the state value table, and the decision continuity and safety of the vehicle are improved.
本发明公开了一种基于隐式时间刻度强化学习的自动驾驶车辆决策方法,包括基于隐式时间刻度的决策模型、改进的强化学习探索方法、基于规则和学习的移动方法以及智能环境车辆模型;基于隐式时间刻度的决策模型将时间刻度信息以隐式的方法导入状态价值表,从而提高决策行为的延续性;改进的强化学习探索方法使用基于预设策略的探索方法、基于反向传播的值表更新方法和基于动量法的局部最优点突破对强化学习的探索效率进行了改进;基于规则和学习的移动方法和智能环境车辆模型可以建立仿真环境对本发明决策的有效性和延续性进行验证。本发明将时间信息隐于状态价值表中,提高了车辆的决策延续性与安全性。
Automatic driving vehicle decision-making method based on implicit time scale reinforcement learning
基于隐式时间刻度强化学习的自动驾驶车辆决策方法
2025-02-18
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 |
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