Neural network is beneficial for obtaining weight, which is a crucial parameter for vehicle in operation. However, the high demand of neural network for real data, and the lack of in-depth research on multi-vehicle weight prediction scenarios, have limited the development of weight prediction. In this paper, relying on the genetic algorithm-optimized BP neural network (GA-BP neural network), a commercial vehicle weight prediction method based on driving simulation data was proposed. The driving data of multiple commercial vehicle types were collected and used to study the weight prediction effect of the proposed method in single-vehicle and multi-vehicle scenarios. The fuzzy processing method of driving simulation data was proposed to process simulation data instead of real data to train the model, which effectively solves the problem of high demand for real data in neural network method. The study shows that proposed method can achieve weight prediction deviation of 2.87% for single-vehicle model and 4.63% for multi-vehicle model.

中文翻译：

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一种基于驾驶模拟数据的商用车重量预测方法

神经网络有利于获得权重，权重是车辆运行的关键参数。然而，神经网络对真实数据的高要求，以及缺乏对多车辆重量预测场景的深入研究，限制了重量预测的发展。本文依托遗传算法优化的BP神经网络（GA-BP神经网络），提出了一种基于驾驶模拟数据的商用车重量预测方法。采集多种商用车类型的驾驶数据，研究该方法在单车和多车场景下的权重预测效果。提出驾驶仿真数据的模糊处理方法，用仿真数据代替真实数据来训练模型，有效解决了神经网络方法对真实数据要求高的问题。研究表明，该方法对单车模型的权重预测偏差为2.87%，对多车模型的权重预测偏差为4.63%。