Electrophysiological frequency domain analysis of driver passive fatigue under automated driving conditions

Zhang, Yijing; Ma, Jinfei; Zhang, Chi; Chang, Ruosong · 2021 · Crossref

DOI: 10.1038/s41598-021-99680-4

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Summary

This study investigates the physiological and behavioral markers of passive fatigue in drivers during automated driving, specifically examining how automation level and scenario complexity influence fatigue onset. Passive fatigue, distinct from active fatigue, arises from low mental workloads and monotonous tasks, a condition increasingly relevant as vehicle automation reduces driver engagement. The research aimed to quantify the co-variation between mental workload and passive fatigue and to establish a precise electrophysiological threshold for detecting this state. The experiment involved 48 participants (24 men, 24 women, aged 20–35) who underwent a 1-hour simulated driving session. The design was a 2 (driving mode: automated vs. manual) × 2 (scenario complexity: monotonous vs. engaging) × 6 (time stages: 0–10 to 50–60 min) mixed model. Data collection included 64-channel EEG recordings, performance on a detection-response task (DRT), and subjective assessments using the NASA-TLX and Swedish Occupational Fatigue Inventory (SOFI). EEG data were preprocessed to isolate alpha band power (8–13 Hz) at parietal electrodes (P3, Pz, P4), and Receiver Operating Characteristic (ROC) analysis was used to determine a critical alpha power threshold for distinguishing awake states from passive fatigue. Results indicated that in automated driving under monotonous conditions, passive fatigue emerged significantly after 40 minutes. At this point, alpha power increased significantly compared to manual driving, while DRT accuracy decreased and reaction times slowed. Subjective reports confirmed higher frustration and fatigue scores in the monotonic automated condition. ROC analysis identified a critical alpha power threshold of 0.000852, yielding 90% sensitivity and 70% specificity, with an area under the curve of 0.78. This threshold effectively differentiated the passive fatigue state (automated, monotonic, stages 4–6) from the awake state (manual, monotonic, stages 4–6). The findings confirm that low mental workload in automated, simple scenarios leads to measurable passive fatigue within 40 minutes. The significance of this work lies in providing a validated, objective electrophysiological metric for passive fatigue detection. By establishing a specific alpha power threshold, the study offers a tool for real-time monitoring systems in automated vehicles. The findings underscore the need for vigilance maintenance strategies to keep driver mental load within an optimal zone, preventing the decline in situational awareness associated with passive fatigue. This contributes to the development of safer human-machine interfaces for automated driving systems.

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Topics

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• drowsiness detection algorithms
• drowsiness
• time on task
• truck driver fatigue
• vigilance
• sleep deprivation

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: theory or model