基于驾驶绩效的车载信息系统操作分心判定模型  

Driver Distraction Judging Model Under In-vehicle Information System Operation Based on Driving Performance

作　　者：马艳丽[1] 顾高峰 高月娥[1] 马勇[3] 

机构地区：[1]哈尔滨工业大学交通科学与工程学院,黑龙江哈尔滨150090 [2]广州市交通规划研究院,广东广州510030 [3]长安大学汽车学院,陕西西安710064

出　　处：《中国公路学报》2016年第4期123-129,共7页China Journal of Highway and Transport

基　　金：国家自然科学基金项目(51108136);中央高校基本科研业务费专项资金项目(310822161006)

摘　　要：为了探寻操作车载信息系统(IVIS)时的驾驶分心识别方法,采用方差分析法验证了驾驶绩效指标作为驾驶分心判定变量的有效性;开展了基于驾驶绩效的IVIS操作分心试验,获取驾驶绩效指标数据及前方道路场景视频,根据IVIS操作条件下的驾驶绩效数据,采用支持向量机(SVM)分类算法构建了基于驾驶绩效的IVIS操作分心判定模型,并利用试验数据验证了模型的有效性。结果表明:采用SVM模型能够对驾驶人的分心状态进行判定,使用RBF核函数时,驾驶分心识别准确率为89.86%,高于使用Sigmoid核函数和多项式核函数时的正确率;该模型能够有效地对驾驶人的分心状态进行判定,可为驾驶分心控制策略提供数据支持。In order to explore the recognition method of distracted driving in operating in-vehicle information system（IVIS）,the effectiveness of driving performance indicator as the evaluation variable of distracted driving was verified by using variance analysis method.Experiments under IVIS operation based on driving performance were carried out,and road scene video and driving performance indicators data were obtained.According to the driving performance data under IVIS operation,using support vector machine（SVM）classification algorithm,the driver distraction judging model was built based on vehicle driving performance under IVIS operation,and the effectiveness of the model was validated by experimental data.The results show that SVM model can be used to determine the driver distraction state.When using RBF kernel function,the recognition accuracy of driving distraction is 89.86%,higher than those by using Sigmoid and polynomial kernel function.The model can effectively determine the driver distraction state,which can provide data support for driving distraction control strategy.

关 键 词：交通工程 分心 支持向量机 判定模型 驾驶绩效 车载信息系统 

分 类 号：U491.254[交通运输工程—交通运输规划与管理]