自动驾驶条件下驾驶员被动疲劳的电生理频域分析。

School of Psychology, Liaoning Normal University, Dalian, 116029, China.

School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.

Sci Rep. 2021 Oct 13;11(1):20348. doi: 10.1038/s41598-021-99680-4.

With the continuous improvement of automated vehicles, researchers have found that automated driving is more likely to cause passive fatigue. To explore the impact of automation and scenario complexity on the passive fatigue of a driver, we collected electroencephalography (EEG), detection-response task (DRT) performance, and the subjective report scores of 48 drivers. We found that in automated driving under monotonic conditions, after 40 min, the alpha power of the driver's EEG indicators increased significantly, the accuracy of the detection reaction task decreased, and the reaction time became slower. The receiver characteristic curve was used to calculate the critical threshold of the alpha power during passive fatigue. The determination of the threshold further clarifies the occurrence time and physiological characteristics of passive fatigue and improves the passive fatigue theory.

随着自动驾驶技术的不断发展，研究人员发现自动驾驶更容易导致被动疲劳。为了探索自动化和场景复杂性对驾驶员被动疲劳的影响，我们收集了 48 名驾驶员的脑电图（EEG）、检测-响应任务（DRT）表现和主观报告评分。我们发现，在单调条件下的自动驾驶中，40 分钟后，驾驶员脑电图指标的阿尔法功率显著增加，检测反应任务的准确性降低，反应时间变得更慢。我们使用接收机特征曲线计算了被动疲劳期间阿尔法功率的临界阈值。该阈值的确定进一步阐明了被动疲劳的发生时间和生理特征，完善了被动疲劳理论。