The invention relates to the technical field of vehicle behavior decision making, in particular to a vehicle following behavior decision making method based on an improved DDPG algorithm, which comprises the following steps: screening out vehicle following event data meeting conditions according to an original natural data set; the method comprises the following steps of: screening out vehicle following events, selecting proper driving behavior data characteristics in a data set, establishing a vehicle following model based on an improved DDPG algorithm, setting a state space, setting an action space and a reward function R, comprehensively considering three factors, and ending a training process when training and strategy convergence or a preset training round number is reached; according to the method, storage and reuse of historical empirical data are facilitated through dual empirical playback pools, two independent playback pools are introduced, delayed interval random sampling is carried out to carry out data acquisition, decision making efficiency is planned to be improved, a reward function comprehensively considers three aspects, namely safety, efficiency and comfort, so that an intelligent agent can consider comprehensive performance of a current time step, and decision making efficiency is improved. Therefore, the high-precision vehicle following behavior decision is achieved.
本发明涉及车辆行为决策的技术领域,特别是涉及一种基于改进DDPG算法的车辆跟驰行为决策方法,包括:根据原始自然数据集,筛选出符合条件的跟车事件数据,筛选出的跟车事件、选取数据集中合适的驾驶行为数据特征、建立基于改进DDPG算法的车辆跟驰模型、设置状态空间、设置动作空间、奖励函数R综合考虑了三方面因素、训练和策略收敛或达到预设的训练轮数时,终止训练过程;其通过双重经验回放池帮助存储和重用历史经验数据,引入两个独立回放池和进行延迟的间隔随机采样进行数据获取拟提高决策的效率,奖励函数综合考虑了三方面,安全性、效率性和舒适性,使得智能体能考虑到当前时间步的综合表现,从而达到精度较高的车辆跟驰行为决策的。
Vehicle following behavior decision-making method based on improved DDPG algorithm
一种基于改进DDPG算法的车辆跟驰行为决策方法
12.11.2024
Patent
Elektronische Ressource
Chinesisch
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