Examining physiological parameters of mental workload in the cockpit: a multimethod approach

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

With increasing automation in the cockpit and the focus on adaptive assistance systems that provide tailored assistance to the pilots, it becomes increasingly relevant to monitor the pilots’ state. While many different psychophysiological measurement methods have been utilized in previous studies, they each have drawbacks and limitations that need to be overcome to provide a valid and reliable operator state assessment. In the present study, we investigated the assessment of mental workload (MWL) using a multimethod approach and combination of different physiological measurements (including neurophysiological, central and peripheral measures as well as eye-tracking) during a simulated flight task. A 60-minute simplified flight task was conducted with 14 commercial airline pilots in four difficulty levels, while their physiological, performance and self-report data were collected. Our analyses reveal that especially neurophysiological measures (electroencephalography [EEG], functional near-infrared spectroscopy [fNIRS]) and measures of heart rate (HR) and heart rate variability (HRV, specifically avNN) were able to differentiate the induced levels of MWL, and that different measures performed better in low and others in high MWL levels. Thus, we argue for a combination of physiological measures to assess MWL for future cockpit applications.

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