基于支持向量机的智能轮胎路面辨识算法

Road Identification Algorithm of Intelligent Tire Based on Support Vector Machine

本文中提出一种基于支持向量机的智能轮胎算法,用于预测分类轮胎-路面间峰值附着系数。基于MEMS三轴加速度计搭建智能轮胎硬件系统,并在3种不同附着情况的路面上进行实车测试;提取径向与侧向加速度信号时域频域统计特征,采用主成分分析法进行特征降维;基于降维后特征参数,应用支持向量机进行分类训练;最后利用参数优化完毕的支持向量机分类器对路面峰值附着系数进行辨识。实车测试结果表明:所提出的算法可以实现路面状态的快速估计,从而为车辆控制系统提供道路的关键信息。与传统的附着系数辨识算法相比,本文中提出的方法更直接、稳定和可靠,而且不需要车辆进行加速、制动或转向。该方法泛化能力强,适用范围广,具有潜在的工程价值。

In this paper,an intelligent tire algorithm based on support vector machine(SVM)is proposed to predict and classify tire-road friction coefficient.Firstly,the intelligent tire hardware system is developed based on MEMS three-dimensional accelerometer and field test is carried out on three types of road with different adhesion conditions.The radial and lateral acceleration signals are obtained and statistical features in time and frequency domain are extracted.Then,the feature dimension is reduced by principal component analysis(PCA).Based on the feature parameters after dimension reduction,support vector machine is applied for classification training.Finally,the SVM classifier with optimized parameters is used to identify the peak adhesion coefficient.Vehicle field test results show that the proposed algorithm can realize quick estimation of road state,thus providing the key information of road for vehicle control system.Compared with the traditional identification algorithm of adhesion coefficient,the proposed method in this paper is more direct,stable and reliable which does not need acceleration,braking or steering conditions.The method has strong generalization ability,wide application range which is of great potential engineering value.