基于换道行为的驾驶分心识别方法
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摘要
为预防驾驶分心导致的交通事故,利用径向基函数(RBF)神经网络模型,研究驾驶分心识别方法。通过驾驶模拟试验,分析驾驶人分别在正常驾驶、手持接听电话和免提接听电话等3种状态下执行车辆换道操作时的驾驶行为,构建基于最小正交二乘法(OLS)的RBF神经网络驾驶分心识别模型,用于判定驾驶人是否处于分心状态。研究表明:驾驶分心对换道过程中车辆的纵向速度、横向速度、横向加速度、方向盘转角、方向盘转速和油门开度等6项驾驶绩效参数有显著影响,所构建模型的平均识别正确率达到88. 7%,可准确识别驾驶人的分心状态,为分心事故预防提供理论支撑。
In order to prevent traffic accidents caused by driver distraction,a method was developed for distraction detection by using the radial basis function neural network model. The driving behavior during the lane change process was analyzed through driving simulation experiments. On the basis of the data from the experiments,effects of driver's three states,normal driving,hand-held answering phone and hand-free answering phone on the lane changing behavior were studied. An RBF neural network model was built based on orthogonal least square( OLS) method to monitor whether the driver is driving distracted. The results show that driving distraction during the lane change process has a significant influence on 6 driving performance parameters such as vehicle ' s longitudinal velocity,lateral velocity,lateral acceleration,steering angle,steering wheel velocity and opening degree of accelerator,and the average detection accuracy of the model reaches 88.7%,which could accurately identify the distracted state of the driver and provide theoretical support for the prevention of distraction accidents.
In order to prevent traffic accidents caused by driver distraction,a method was developed for distraction detection by using the radial basis function neural network model. The driving behavior during the lane change process was analyzed through driving simulation experiments. On the basis of the data from the experiments,effects of driver's three states,normal driving,hand-held answering phone and hand-free answering phone on the lane changing behavior were studied. An RBF neural network model was built based on orthogonal least square( OLS) method to monitor whether the driver is driving distracted. The results show that driving distraction during the lane change process has a significant influence on 6 driving performance parameters such as vehicle ' s longitudinal velocity,lateral velocity,lateral acceleration,steering angle,steering wheel velocity and opening degree of accelerator,and the average detection accuracy of the model reaches 88.7%,which could accurately identify the distracted state of the driver and provide theoretical support for the prevention of distraction accidents.
引文
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[10]刘卓凡,付锐,程文冬,等.驾驶人视觉分心与认知分心研究综述[J].中国安全科学学报,2015,25(7):29-34.LIU Zhuofan,FU Rui,CHENG Wendong,et al. Overview of researches on drivers'visual distraction and cognitive distraction[J]. China Safety Science Journal,2015,25(7):29-34.
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[12] CHAN M,SINGHAL A. Emotion matters:implications for distracted driving[J]. Safety Science,2015,72:302-309.
[2]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.MA Yanli,GU Gaofeng,GAO Yue'e,et al. Driver distraction judging model under in-vehicle information system operation based on driving performance[J]. China Journal of Highway and Transport,2016,29(4):123-129.
[3] WOLLMER M,BLASCHKE C,SCHINDL T,et al. Online driver distraction detection using long short-term memory[J]. IEEE Transactions on Intelligent Transportation Systems,2011,12(2):574-582.
[4]高岩,罗毅,尤志栋,等.手机操作类型对驾驶人跟车行为的影响[J].中国公路学报,2018,31(4):1-9.GAO Yan, LUO Yi, YOU Zhidong, et al. Influence of operating types of smartphone on drivers'car-following behavior[J]. China Journal of Highway and Transport,2018,31(4):1-9.
[5]成波,孟传,张伟.基于机器视觉的驾驶员注意力状态监测技术研究[J].汽车工程,2009,31(12):1 137-1 140.CHENG Bo,MENG Chuan,ZHANG Wei. A study on driver distraction detection technology based on computer vision[J]. Automotive Engineering,2009,31(12):1 137-1 140.
[6] LIN C T,CHEN S A,KO L W,et al. EEG-based brain dynamics of driving distraction[C]. IEEE International Joint Conference on Neural Networks,2011:1 497-1 500.
[7] HORREY W J,LESCH M F,GARABET A,et al. Distraction and task engagement:how interesting and boring information impact driving performance and subjective and physiological responses[J]. Applied Ergonomics,2017,58:342-348.
[8] SATHYANARAYANA A,NAGESWAREN S,GHASEMZADEH H,et al. Body sensor networks for driver distraction identification[C]. IEEE International Conference on Vehicular Electronics and Safety,2008:120-125.
[9]张辉,钱大琳,邵春福,等.模拟驾驶环境下驾驶人分心状态判别[J].中国公路学报,2018,31(4):43-51.ZHANG Hui, QIAN Dalin, SHAO Chunfu, et al. Driver's distraction states identification in simulation driving environment[J]. China Journal of Highway and Transport,2018,31(4):43-51.
[10]刘卓凡,付锐,程文冬,等.驾驶人视觉分心与认知分心研究综述[J].中国安全科学学报,2015,25(7):29-34.LIU Zhuofan,FU Rui,CHENG Wendong,et al. Overview of researches on drivers'visual distraction and cognitive distraction[J]. China Safety Science Journal,2015,25(7):29-34.
[11] LIANG Y,LEE J D. Combining cognitive and visual distraction:less than the sum of its parts[J]. Accident Analysis and Prevention,2010,42(3):881-890.
[12] CHAN M,SINGHAL A. Emotion matters:implications for distracted driving[J]. Safety Science,2015,72:302-309.