The invention provides a universal perception model training method and device and an automatic driving vehicle. The method comprises the following steps: establishing a plurality of first models configured with a first training threshold value, wherein a plurality of sensors in the same type are located at different positions of a vehicle; a plurality of training arrays are adopted to train a corresponding first model, so that the first model selects effective first training data according to a first training threshold value and the first random number to carry out machine learning, a plurality of feature extraction models are obtained, and the training arrays comprise the first training data of each sensor in the corresponding type; the first training data comprises sensor data and road condition feature information, the road condition feature information comprises a plurality of first grids and road condition features, the road condition features are located in the first grids mapped with the corresponding sensors, and the sensor data carries a first random number representing whether the sensor data is valid or not; and determining a universal perception model at least according to the plurality of feature extraction models.
本申请提供了一种通用感知模型的训练方法、装置和自动驾驶车辆。该方法包括:建立多个配置有第一训练阈值的第一模型,同一个类型下传感器有多个且位于车辆的不同位置;采用多个训练数组训练对应的第一模型,使得第一模型根据第一训练阈值和第一随机数选择有效的第一训练数据进行机器学习,得到多个特征提取模型,训练数组包括对应的类型下各传感器的第一训练数据,第一训练数据包括传感器数据和路况特征信息,路况特征信息包括多个第一网格和路况特征,路况特征位于与对应的传感器映射的第一网格中,传感器数据携带有表征自身是否有效的第一随机数;至少根据多个特征提取模型,确定通用感知模型。
Training method and device of universal perception model and automatic driving vehicle
通用感知模型的训练方法、装置和自动驾驶车辆
2024-11-22
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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