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Discriminant Model of Driving Distraction During Mobile Phone Conversation Based on Eye Movements

机译:基于眼球运动的移动电话交谈中驾驶分心的判别模型

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In order to investigate the characteristics of drivers' eye movements during distracted driving caused by mobile phone conversation and establish a driving distraction discriminant model, a driving simulation experiment was conducted. The eye movement index data were collected by eye tracker under different traffic scenes which include normal driving and perform simple or complex conversation secondary task on the urban road and freeway, then the variance analysis was used to analyze the characteristics. Finally, according to the characteristics of drivers' eye movements, a driving distraction discriminant model based on fisher discriminant analysis was constructed for different road types. The ANOVA results showed that the effectiveness of road type and conversation task on the cumulative proportion of the driver's focus on the area of interest in the front road is not statistically significant. However, the average duration of the driver's attention under urban road scene is significantly higher than that of the freeway, and with the increasing of difficulty of driving task, the average duration of attention increased significantly. In addition, the road type and conversation task significantly influenced the change range of pupil area. The accuracy rate of the discriminant model is 75.2% for the driving distraction on urban roads, and 78.3% for the distraction on freeway.
机译:为了探讨由移动电话交谈引起的分散注意力驾驶期间驾驶员眼球运动的特征,并建立驾驶分散判别模型,进行了驾驶模拟实验。眼睛运动索引数据由眼跟踪器在不同的交通场景下收集,包括正常驾驶,并在城市道路和高速公路上执行简单或复杂的谈话二次任务,然后使用方差分析来分析特征。最后,根据驾驶员眼球运动的特征,基于Fisher判别分析的驾驶分散判别模型被构建为不同的道路类型。 ANOVA结果表明,道路类型和对话任务的有效性对驾驶员焦点对前道上兴趣领域的累积比例的累计比例没有统计学意义。然而,驾驶员在城市道路场景下的平均持续时间明显高于高速公路,随着驾驶任务的难度的增加,平均关注的持续时间显着增加。此外,道路类型和谈话任务显着影响了瞳孔区域的变化范围。判别模型的准确率为75.2%,以便在城市道路上施加分心,78.3%用于高速公路分心。

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