The invention relates to an intelligent parking method based on model reinforcement learning, which uses Monte Carlo tree search, a vehicle kinematics model, an action classification network and a state value fitting network, and specifically comprises the following steps: S1, combining the Monte Carlo tree search with the action classification network and the vehicle kinematics model to obtain aparking data pre-training model; S2, training a state value fitting network according to parking data generated by the parking pre-training model; S3, combining the trained state value fitting networkwith a Monte Carlo tree search and action classification network to form an online driving strategy model; and S4, enabling the parking on-line driving strategy model to receive the parking space andthe vehicle motion information in a rolling time domain mode, generate a control instruction at each time interval, and send the control instruction to a vehicle motion control module to control thetarget vehicle to complete parking. Compared with the prior art, the method has the advantages that the final parking course angle and success rate are better, the influence of the accuracy of the vehicle model on the final parking effect is reduced, and the like.
本发明涉及一种基于模型强化学习的智能泊车方法,使用蒙特卡罗树搜索、车辆运动学模型、动作分类网络和状态值拟合网络,具体包括以下步骤:S1.蒙特卡罗树搜索结合动作分类网络和车辆运动学模型获取泊车数据预训练模型;S2.根据泊车预训练模型生成的泊车数据训练状态值拟合网络;S3.训练完成的状态值拟合网络与蒙特卡罗树搜索、动作分类网络相结合,构成在线驾驶策略模型;S4.泊车在线驾驶策略模型以滚动时域方式,接收库位和车辆运动信息,在每个时间间隔产生一个控制指令,并发送至车辆运动控制模块来控制目标车辆完成泊车。与现有技术相比,本发明具有更好的最终泊车航向角和成功率、减少车辆模型的精确度对最终泊车效果的影响等优点。
Intelligent parking method based on model reinforcement learning
一种基于模型强化学习的智能泊车方法
2021-02-12
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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