手机打车软件操作驾驶分心检测模型研究
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摘要
为探寻操作手机打车软件时的驾驶分心识别方法,本文开展模拟驾驶实验,采集了驾驶员在不同驾驶状态下的驾驶行为参数,通过对参数的统计分析,确立分心检测参数集。采用支持向量机分类算法理论构建基于驾驶绩效的分心检测模型,并利用实验数据验证模型的有效性。结果表明:该模型对驾驶员视觉分心驾驶行为检测率最高,正常驾驶行为次之,对认知分心驾驶行为的检测能力最弱,模型的平均检测正确率为86.67%,检测效果较好,可用于驾驶分心检测。
For efficient detection of distracted-driving when using smartphone taxi-hailing applications, the paper carries out simulated driving experiments and collects the driving behavior parameters under different driving conditions. The set of distracting parameters is established via a statistical analysis of the collected parameters. Support vector machine is then used to construct the distracted-driving detection model based on the driving performance. The proposed model is verified using the experiment data. The results show that the model has the highest detection rate for visual-related distracted driving, followed by normal driving behavior, and cognitive-related distracted driving. The average detection rate of the model is 86.67%.
For efficient detection of distracted-driving when using smartphone taxi-hailing applications, the paper carries out simulated driving experiments and collects the driving behavior parameters under different driving conditions. The set of distracting parameters is established via a statistical analysis of the collected parameters. Support vector machine is then used to construct the distracted-driving detection model based on the driving performance. The proposed model is verified using the experiment data. The results show that the model has the highest detection rate for visual-related distracted driving, followed by normal driving behavior, and cognitive-related distracted driving. The average detection rate of the model is 86.67%.
引文
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[2]LIU B S,LEE Y H.Effects of car-phone use and aggressive disposition during critical driving maneuvers[J]Transportation Research Part F:Traffic Psychology and Behaviour,2005,8(4):369-382.
[3]COLLET C,GUILLOT A,PETIT C.Phoning while driving I:a review of epidemiological,psychological,behavioural and physio-logical studies[J].Ergonomics,2010,53(5):589-601.
[4]李胜江.驾驶人视觉注意力分散检测方法研究[D].长春:吉林大学,2015.
[5]WOLLMER M,BLASCHKE C,SCHINDL T,et al.Online driver distraction detection using long short-term memory[J].Intelligent Transportation Systems,IEEE Transa-ctions on,2011,12(2):574-582.
[6]马艳丽,顾高峰,高月娥,等.基于驾驶绩效的车载信息系统操作分心判定模型[J].中国公路学报,2016,29(4):123-129.
[7]马勇,石涌泉,付锐,等.驾驶人分心时长对车道偏离影响的实车试验[J].吉林大学学报,2015,45(4):1095-1101.
[8]李宏汀,刘彦宇,李文书.驾驶中使用手机对驾驶员行为安全绩效影响综述[J].中国安全科学学报,2013,23(1):16-21.
[9]吴佳华,桂玉峰,刘畅,等.基于驾驶模拟器的不同驾驶条件下手机使用的可靠度分析[J].交通与运输,2011,(12):176-180.
[10]唐智慧,党姗,郑伟皓.打车软件的使用对驾驶安全的影响[J].交通运输工程与信息学报,2017,(1):22-27.
[11]黄成龙.成都市三环路交通指路标志设置合理性研究[D].成都:西南交通大学,2015.
[12]彭璐,支持向量机分类算法研究与应用[D].长沙:湖南大学,2007.
[13]LIANG Y L,REYES M L,LEE J D.Real-time detection of driver cognitive distraction using support vector machines[J],IEEE Transactions on Intelligent Transportation Systems,2007,8(2):340-350.
[14]张艳,吴玲.基于支持向量机和交叉验证的变压器故障诊断[J].中国电力,2012,45(11):52-55.