Investigating the impact of driving workload on fatigue and performance for the purpose of road safety

Serra, Patrizia; Murru, Alessandro; Podda, Simone; Porta, Mattia; Fancello, Gianfranco · 2026 · Crossref

DOI: 10.54941/ahfe1007851

archive: archived pipeline: cataloged verified

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Summary

This study addresses the challenge of quantifying driver fatigue, a significant contributor to road accidents that is often difficult to measure due to its subjective nature and inconsistent reporting. The research investigates how fluctuations in driving workload, driven by environmental stressors, influence the development of fatigue and subsequent driving performance. Motivated by the need for a holistic assessment framework, the authors aim to move beyond isolated metric analysis by integrating physiological, psychomotor, and subjective data to provide a comprehensive evaluation of driver state. The experimental design utilized a high-fidelity car simulator with thirty non-professional drivers (mean age 34, 14 years of driving experience). Participants underwent a within-subjects protocol comparing two conditions: optimal driving scenarios with regular traffic and favorable weather, and critical scenarios characterized by heavy traffic, unpredictable events, and adverse weather. To mitigate learning and order effects, sessions were counterbalanced and conducted on separate days. Data acquisition employed a multimodal approach, combining continuous physiological monitoring via electroencephalography (EEG) and electrocardiography (ECG) with point-in-time psychomotor assessments, including reaction time (RT) and Flicker Fusion tests. Subjective workload was measured using the NASA-TLX questionnaire. The results indicate that critical driving conditions significantly impact specific fatigue markers. The Flicker Fusion ascending threshold showed a statistically significant reduction under stressful conditions, signaling central nervous system fatigue and diminished perceptual efficiency. Subjective NASA-TLX scores increased during critical sessions, confirming higher perceived mental workload. While EEG-derived attention and stress scores decreased slightly, and reaction times showed a non-significant increase, heart rate and heart rate variability did not differ significantly between conditions. However, the absence of parasympathetic recovery in critical conditions suggests drivers exerted greater physiological effort to maintain performance. Whole-body vibrations also increased modestly in critical scenarios, reflecting higher mechanical load. The study concludes that environmental complexity accelerates the onset of mental fatigue, detectable through specific psychomotor and subjective metrics even when overall performance remains stable. The findings highlight the risk of latent strain, where drivers may compensate for fatigue through increased effort, masking underlying vulnerability to sudden failure. This integrated framework offers valuable insights for designing targeted road safety interventions, infrastructure improvements, and tailored training programs. Future research will focus on developing real-time data synchronization algorithms to combine multi-sensor information for more precise fatigue detection.

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Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

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• workload measurement
• stress driving
• drowsiness detection algorithms
• mental demand
• time on task
• drowsiness

Information type

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• Empirical Findings: physiological data
• Methodological Resource: validation psychometrics
• Theoretical Contribution: theory or model