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Analysis of Driver’s EEG Given Take-Over Alarm in SAE Level 3 Automated Driving in a Simulated Environment
International Journal of Automotive Technology ( IF 1.6 ) Pub Date : 2020-02-20 , DOI: 10.1007/s12239-020-0070-3
Jiwon Lee , Ji Hyun Yang

As partially automated driving vehicles are set to be mass produced, there is an increased necessity to research situations where such partially automated vehicles become unable to drive. Automated vehicles at SAE Level 3 cannot avoid a take-over between the human driver and vehicle system. Therefore, how the system alerts a human driver is essential in situations where the vehicle autonomous driving system is taken over. The present study delivered a take-over transition alert to human drivers using diverse combinations of visual, auditory, and haptic modalities and analyzed the drivers’ brainwave data. To investigate the differences in indexes according to the take-over transition alert type, the independent variable of this study, the nonparametric test of Kruskal-Wallis was performed along with Mann-Whitney as a follow-up test. Moreover, the pre/post-warning difference in each index was investigated, and the results were reflected in ranking effective warning combinations and their resulting scores. The visual-auditory-haptic warning scored the highest in terms of various EEG indexes, to be the most effective type of take-over transition alert. Unlike most preceding studies analyzing post-take-over-alert human drivers’ response times or vehicle behavior, this study investigates drivers’ brainwave after the take-over warning.

中文翻译:

在模拟环境中,SAE 3级自动驾驶的驾驶员EEG给出了接管警报

由于将部分自动驾驶车辆定为批量生产,因此越来越需要研究这种部分自动驾驶车辆无法驾驶的情况。SAE 3级的自动驾驶汽车无法避免驾驶员和车辆系统之间的接管。因此,在接管车辆自动驾驶系统的情况下,系统如何向驾驶员发出警报是至关重要的。本研究使用视觉,听觉和触觉方式的各种组合向驾驶员发出了过渡过渡警报,并分析了驾驶员的脑电波数据。为了根据接管过渡警报类型(本研究的独立变量)调查指标的差异,对Kruskal-Wallis进行了非参数检验,并与Mann-Whitney进行了后续检验。此外,我们调查了每个指标在预警前后的差异,并将结果反映在对有效警告组合及其得分的排名中。视觉听觉触觉警告在各种EEG指数方面得分最高,是最有效的过渡过渡警报类型。与以往大多数分析接管后警报的人类驾驶员的响应时间或车辆行为的研究不同,本研究调查了接管警告后驾驶员的脑电波。
更新日期:2020-02-20
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