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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Driver Workload Characteristics Analysis Using EEG Data From an Urban Road
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Driver Workload Characteristics Analysis Using EEG Data From an Urban Road

机译：使用来自城市道路的EEG数据进行驾驶员工作量特征分析

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摘要

The main cause of traffic accidents is drivers' human errors such as cognitive, judgment, and execution errors. To mitigate drivers' human errors, research on the measurement and quantification of driver workload as well as the development of smart vehicles is needed. Drivers' behavior while driving includes driving straight, turning left or right, U-turns, rapid acceleration, rapid deceleration, and changing lanes. To measure and quantify a driving workload, both the subjective workload and the behavior workload caused by varied driving behaviors should be taken into account on the basis of understanding the visual, auditory, cognitive, and psychomotor characteristics of the driving workload. In this paper, we analyze electroencephalogram (EEG) data collected through an urban road driving test. To overcome large deviations of EEG values among drivers, we used EEG variation rates instead of raw EEG values. We extracted five kinds of behavior sections from the data: left-turn section, right-turn section, rapid-acceleration section, rapid-deceleration section, and lane-change section. We then selected a reference section for each of these behavior sections and compared EEG values from the behavior sections with those from the reference sections to calculate the EEG variation rates, after which we made the statistical analysis. The analysis results of this study are being used to explain the cognitive characteristics of a driving workload caused by drivers' behavior in the vehicle information system, which will provide information for safe driving by taking into account the driving workload.
机译：交通事故的主要原因是驾驶员的人为错误，例如认知，判断和执行错误。为了减轻驾驶员的人为错误，需要对驾驶员工作量的测量和量化以及智能车辆的开发进行研究。驾驶员在驾驶时的行为包括直行，左转或右转，掉头，快速加速，快速减速和改变车道。为了衡量和量化驾驶工作量，应在理解驾驶工作量的视觉，听觉，认知和心理运动特征的基础上，考虑主观工作量和由各种驾驶行为引起的行为工作量。在本文中，我们分析了通过城市道路驾驶测试收集的脑电图（EEG）数据。为了克服驾驶员之间的EEG值存在较大偏差，我们使用EEG变化率代替原始EEG值。我们从数据中提取了五种行为部分：左转部分，右转部分，快速加速部分，快速减速部分和换道部分。然后，我们为每个行为部分选择一个参考部分，并将行为部分中的EEG值与参考部分中的值进行比较，以计算EEG变化率，然后进行统计分析。这项研究的分析结果正在被用来解释在车辆信息系统中由驾驶员的行为引起的驾驶工作量的认知特征，这将通过考虑驾驶工作量为安全驾驶提供信息。

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