Driver Distraction Assessment Using Driver Modeling
Driver Distraction Assessment Using Driver Modeling
复制标题
使用驾驶员建模进行驾驶员分心评估
DOI：
10.1109/smc.2013.629
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Bin Yang
中科院分区：
文献类型：
--
作者：
Peter Hermannstadter;Bin Yang
在自适应驾驶辅助或监控等应用中，对单个驾驶员的特征描述越来越受关注。用控制理论的驾驶员模型来描述驾驶员是一种很有前途的方法。本文将文献中采用的驾驶员模型应用于分心驾驶实验的实际道路驾驶中，以评估驾驶员的状态。控制理论驱动模型具有预期和补偿跟踪成分，以及加工延迟和神经肌肉运动成分。分心实验数据包括真实道路驾驶的视觉运动和听觉辅助任务，以及参考驾驶。采用预测误差识别的方法，从11个驾驶员的驾驶数据中连续独立地估计模型参数。我们评估了驱动模型参数的分布和估计的驱动模型的预测能力。估计的驾驶员模型参数根据驾驶任务反映分心驾驶行为。实验结果表明，驾驶员模型参数和预测性能与驾驶员分心显著相关。
Characterizing individual human drivers is of increasing interest for applications like adaptive driver assistance or monitoring. Describing the human driver by means of control-theoretic driver models constitutes a promising approach. In this paper, we apply a driver model adopted from literature to real-road driving of a distraction experiment in order to assess the driver state. The control-theoretic driver model features an anticipatory and a compensatory tracking component as well as a processing delay and a neuromuscular motor component. The distraction experiment data comprises real road driving with a visuomotor and an auditory secondary task, as well as reference driving. By means of prediction error identification, we continuously and individually estimate the model parameters from driving data of eleven drivers. We evaluate the distributions of the driver model parameters and the predictive capability of the estimated driver models. The estimated driver model parameters reflect distracted driving behavior according to the driving task. As a promising experimental result, the driver model parameters and predictive performance are significantly associated with driver distraction.
影响因子：
20.6
作者：
Miyajima, Chiyomi;Nishiwaki, Yoshihiro;Itakura, Fumitada
通讯作者：
Itakura, Fumitada