基于IDM与RBFNN的组合型车辆低速跟驰模型

目前针对车辆低速跟驰驾驶的建模研究较少。通过最优加权理论将理论驱动型跟驰模型与数据驱动型跟驰模型进行结合，建立了一种基于智能驾驶者模型（IDM）与径向基函数神经网络（RBFNN）的组合型车辆低速跟驰模型。首先对NGSIM公开数据集进行筛选与处理得到基础研究数据；之后分别建立基于IDM与RBFNN的低速跟驰模型，前者侧重于保证跟驰的安全性与舒适性，后者则能够输出与真实值更为相符的预测结果；最后通过改进的最优加权目标函数得到最优组合权重，从而建立起了IDM-RBFNN组合模型。用平均相对误差（MARE）进行了评估，并通过对比分析证明了组合模型具有比单一模型更优的预测效果。

Combination low-speed car-following model based on IDM and RBFNN

So far, there are only a few researches focused on the low-speed car-following model. This paper proposed a combined low-speed car-following model based on intelligent driver model(IDM) and radial basis function neural network(RBFNN). This model combined the theory-driven car-following model and the data-driven car-following model by optimal weighting. The first step of the research was processing the public dataset named NGSIM in order to obtain the low-speed car-following data, and then respectively establishing the low-speed car-following model based on IDM and RBFNN. The former focused on ensuring safety and comfort, while the latter could output the prediction results closer to the actual values. Finally, the optimal combination weights of the IDM-RBFNN combined model were calculated by the improved optimal weighted objective function and then evaluating the model by MARE. The comparation analysis shows that the IDM-RBFNN combined model is better than the single model.