基于支持向量机的制动意图识别方法

Recognition method of braking intention based on support vector machine

探索了一种采用实车试验数据,基于支持向量机(SVM)的制动意图识别方法。选取制动踏板位移、制动踏板力及制动减速度为制动意图识别参数,制动工况分为紧急制动、持续制动和常规制动;搭建制动意图识别整车测试系统,进行了多组制动工况试验,获取识别参数试验数据;构建了制动意图识别SVM模型,选取RBF核函数为SVM核函数,基于k折交叉验证法和网格搜索法对惩罚因子C与核函数参数σ进行了寻优;基于实际试验数据,选取持续制动、常规制动及紧急制动3种制动工况,对所构建的SVM模型进行了离线验证。结果表明:所构建的SVM模型具有较高的制动意图识别准确率,为后续的进一步应用提供了理论基础。

This paper focuses on a recognition method of braking intention based on the test data of real vehicle and the support vector machine(SVM). Brake pedal displacement,brake pedal force and braking deceleration were selected as the identification parameters of brake intention,and the braking conditions were divided into emergency braking,continuous braking and conventional braking. The vehicle test system of braking intention recognition was built,and several groups of brake tests were carried out to obtain the test data of identification parameters. The SVM model of brake intention recognition was constructed,and the RBF kernel function was selected as the SVM kernel function. Based on k-fold cross validation method and grid search method,the penalty factor C and kernel function parameter σ were optimized. Based on the actual test data,three braking conditions,gentle braking,conventional braking and emergency braking,were selected to verify the SVM model. The results show that the SVM model has high recognition accuracy of braking intention,which provides a theoretical basis for further application.