高负荷驾驶任务下驾驶员注意力状态概率模型

郭孜政; 陈崇双; 陈亚青

高负荷驾驶任务下驾驶员注意力状态概率模型

基金项目:

国家自然科学基金资助项目(70422201)

国家863计划项目(2006AA12A104)

详细信息

作者简介:
郭孜政(1982- ),男,博士研究生,研究方向为交通运输规划与管理,电话:13084446033,E-mail:zizhengguo@mars.swjtu.edu.cn

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出版历程
- 收稿日期: 2007-12-24

Probability Model for Driver Attention State under High Driving Load

摘要

摘要: 为研究高负荷驾驶任务下驾驶员注意力状态转移规律,应用更新过程理论构建了短时段注意力状态概率模型,并给出了近似求解方法.对于长时间连续驾驶,该模型在状态转移点处难以满足齐次性特征要求,可用时段分割法处理.用实际数据对模型进行了仿真实验.结果表明,模型整体误差为2.83%.
- 驾驶员 /
- 注意力 /
- 概率模型 /
- 马尔科夫过程理论
Abstract: To study the shifting progress of driver’s attention under high driving load,a probability model for driver’s attention state during a short period and a approximate solution algorithm were presented based on the Markov process theory.This model is not directly applicable to a long period because it does not meet the homogeneous requirement at driving state transition points,This problem could be solved by cutting the long period into small fractions.Practical data were used for simulation,and the result shows that the overall error of the model was 2.83%.
- driver /
- attention /
- probability model /
- Markov process theory

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参考文献(1)

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文章访问数: 1670
HTML全文浏览量: 80
PDF下载量: 100
被引次数: 0