A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving
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Summary
Selective narrative review (Lohani, Payne, Strayer; U. Utah; Frontiers in Human Neuroscience 13:57, 2019, doi:10.3389/fnhum.2019.00057) of psychophysiological measures suitable for assessing cognitive states (workload, distraction, fatigue, drowsiness) in real-world driving as automation increases. The review covers EEG and ERPs, optical imaging (fNIRS), heart rate and heart-rate variability, blood pressure, electrodermal activity (skin conductance), electromyography, thermal imaging, and pupillometry. For each measure the authors describe the physiological basis, methods for measurement in a vehicle context, strengths and limitations, and recommendations for moving from lab to less predictable on-road settings. The discussion synthesizes which measures are best suited to near-real-time assessment of cognitive states for in-vehicle human-machine interfaces and driver support systems.
Key finding
No single psychophysiological measure dominates for in-vehicle workload/fatigue assessment; pupillometry, HRV, and EDA provide complementary signals on different timescales, while EEG/ERP offer the highest temporal resolution but suffer most from motion and electrical artefacts in real-world driving. The review concludes that multi-method physiological monitoring (rather than single-channel reliance) is the most viable path to operational driver-state estimation in semi- and highly-automated vehicles.
Methodology
Narrative selective review (not PRISMA). Coverage spans EEG/ERP, fNIRS, HR/HRV, BP, EDA, EMG, thermal imaging, and pupillometry. For each measure the authors describe physiological basis, in-vehicle measurement methods, sensitivity to cognitive constructs (workload, distraction, fatigue), and challenges of moving from lab to on-road environments. No quantitative meta-analysis.
Quality score: 5 / 5