The invention relates to a training method of a vehicle action prediction model and a vehicle control method, the vehicle action prediction model comprises a current action prediction network and a subsequent action prediction network, the training method comprises the steps of obtaining training data, the training data comprises action data at a first moment and first state data, and second state data at a second moment; inputting the action data at the first moment and the first state data into a current action prediction network, and inputting the second state data into a subsequent action prediction network; performing execution performance prediction on prediction results of the current action prediction network and the subsequent action prediction network based on the first network and the second network; adjusting the parameters of each network according to the execution performance prediction result; the training data is determined based on vehicle motion data and a high-precision map which are respectively generated by a driver driving vehicle, an automatic driving simulation platform and automatic driving track planning. The quality of the training data can be improved, and local optimum is avoided.
本公开涉及车辆动作预测模型的训练方法及车辆控制方法,车辆动作预测模型包括当前动作预测网络和后续动作预测网络,该训练方法包括:获取训练数据,该训练数据包括第一时刻下的动作数据和第一状态数据,以及第二时刻下的第二状态数据;将第一时刻下的动作数据和第一状态数据输入当前动作预测网络,将第二状态数据输入后续动作预测网络;基于第一网络和第二网络分别对于当前动作预测网络和后续动作预测网络的预测结果进行执行表现预测;根据执行表现预测结果调整上述各网络的参数;上述训练数据基于驾驶员驾驶车辆、自动驾驶仿真平台、自动驾驶轨迹规划分别产生的车辆运动数据和高精地图确定。本公开可以提高训练数据的质量,避免陷入局部最优。
Training method of vehicle action prediction model and vehicle control method
一种车辆动作预测模型的训练方法及车辆控制方法
2023-01-20
Patent
Electronic Resource
Chinese
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / 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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