基于守恒高阶模型和支持向量机的多车道车辆换道模型

A Lane Changing Model Based on High Order Conservation Model and Support Vector Machine

针对高速公路车辆换道问题,提出一个多车道车辆换道模型。利用支持向量机(SVM)在多维特征下二分类问题的优势,将SVM和Lagrange坐标下的高阶守恒模型(CHO)结合,通过全离散跟车模型生成原始数据,采用SMOTE(Synthetic Minority Oversampling Technique)算法对数据进行预处理,采用双指标评估度SVM进行训练,建立多车道车辆换道仿真模型。仿真结果表明:基于支持向量机和CHO模型的换道模型,驾驶车能够就当前的驾驶环境,准确地作出决策,有效地模拟高速公路上真实的多车道驾驶情况。

A lane changing model for multi-lane traffic flow is proposed. It makes use of advantages of Support Vector Machine(SVM) in a binary classification problem with multi-dimensional features and combines with Conserved Higher-Order traffic flow model(CHO) in Lagrange coordinates.The original data is generated with a fully discrete car following model and preprocessed by Synthetic Minority Oversampling Technique(SMOTE) algorithm. The SVM is trained with two indexes evaluation. It shows that the lane changing model based on SVM and CHO simulates effectively real multi-lane driving behavior based on current driving environment on expressway.