基于K近邻和支持向量机的醉酒驾驶识别方法的对比分析  

Comparison of Drunk Driving Recognizing Methods Based on KNN and SVM

作　　者：李振龙[1] 韩建龙[1] 赵晓华[1] 朱明浩[1] 董文会[1] 

机构地区：[1]北京工业大学城市交通学院,北京100124

出　　处：《交通运输系统工程与信息》2015年第5期246-251,共6页Journal of Transportation Systems Engineering and Information Technology

摘　　要：醉酒驾驶严重威胁道路交通安全,对醉酒驾驶进行准确识别意义重大.利用驾驶模拟舱进行驾驶实验,提取醉酒驾驶和正常驾驶的驾驶行为参数.首先,通过方差分析和均值分析选取方向盘转角作为识别特征,并采用滑动数据窗求取方向盘转角均值序列,构建识别特征参数;然后,分别采用K近邻(KNN)和支持向量机(SVM)对驾驶状态进行识别,得到两种分类方法在不同道路线形的最高识别准确率及其相对应的最优数据窗;最后,对两种分类方法进行了对比分析.结果表明,SVM对醉酒驾驶的识别性能优于KNN;数据窗对KNN的识别准确率影响显著,对SVM的识别准确率影响不明显.Drunk driving is a serious threat to road traffic safety. It is of great significance to identify drunk driving accurately. The drunk driving experiment is conducted in a driving simulator. The driving behavior parameters under the drunk driving and normal driving are collected. The steering wheel angle is selected as the feature based on analysis of variance and analysis of mean. The average sequence of steering wheel angle is calculated using a sliding data window. KNN and SVM are used to identify the driver＇s state. The optimum data window and the highest recognition accuracy of the two algorithms under different road alignment are obtained. The two classification methods are analyzed. The results show that the recognition performance of the SVM is better than that of the KNN. Data window has a significant effect on the performances of KNN and has no significant effect on the performances of SVM.

关 键 词：智能交通 醉酒驾驶识别 K近邻 支持向量机 数据窗 

分 类 号：U491[交通运输工程—交通运输规划与管理]