Predicting visual distraction using driving performance data.
Predicting visual distraction using driving performance data.
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使用驾驶表现数据预测视觉干扰。
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
C. Ahlstrom
中科院分区：
文献类型：
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作者：
K. Kircher;C. Ahlstrom
行为变量通常被用作次要任务引起的视觉或内部干扰的绩效指标（PI）。本研究的目的是调查是否可以通过在自然环境中驾驶绩效 PI 来预测视觉干扰。此处，视觉干扰是由基于注视的实时干扰检测算法（称为 AttenD）定义的。七名驾驶员在小规模现场操作测试中每人使用一辆仪表车辆一个月。对于 AttenD 检测到的每个视觉干扰事件，计算了方向盘反转率和油门保持等七个 PI。还计算了驾驶员被归类为注意力集中的时间段的相应数据。对于每个 PI，使用不同时间窗口大小（2 - 40 秒）的 t 检验计算分心状态和注意力状态之间的平均值，并选择具有最小结果 p 值的窗口宽度作为最佳窗口宽度。基于优化的 PI，使用逻辑回归来预测驾驶员是否专心或分心。逻辑回归得出的预测正确率为 76%（敏感性 = 77%，特异性 = 76%）。结论是，行为变量与视觉分心之间存在相关性，但这种关系还不足以准确预测驾驶员视觉分心。相反，行为 PI 可能最适合作为基于眼动追踪的算法的补充，以使它们更加准确和稳健。
Behavioral variables are often used as performance indicators (PIs) of visual or internal distraction induced by secondary tasks. The objective of this study is to investigate whether visual distraction can be predicted by driving performance PIs in a naturalistic setting. Visual distraction is here defined by a gaze based real-time distraction detection algorithm called AttenD. Seven drivers used an instrumented vehicle for one month each in a small scale field operational test. For each of the visual distraction events detected by AttenD, seven PIs such as steering wheel reversal rate and throttle hold were calculated. Corresponding data were also calculated for time periods during which the drivers were classified as attentive. For each PI, means between distracted and attentive states were calculated using t-tests for different time-window sizes (2 - 40 s), and the window width with the smallest resulting p-value was selected as optimal. Based on the optimized PIs, logistic regression was used to predict whether the drivers were attentive or distracted. The logistic regression resulted in predictions which were 76 % correct (sensitivity = 77 % and specificity = 76 %). The conclusion is that there is a relationship between behavioral variables and visual distraction, but the relationship is not strong enough to accurately predict visual driver distraction. Instead, behavioral PIs are probably best suited as complementary to eye tracking based algorithms in order to make them more accurate and robust.