A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring

Sriranga, Ashwini Kanakapura; Lu, Qian; Birrell, Stewart · 2023 · Crossref

DOI: 10.3390/s23042214

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Summary

This systematic review addresses the growing need to monitor driver mental workload (MWL) in conditionally automated vehicles (Level 3 automation), where drivers must remain alert to resume control during Take Over Requests (TORs). Fluctuations in driving demands alter MWL, potentially impairing a driver’s ability to take over the vehicle. The paper aims to synthesize literature on objective, in-vehicle physiological indices—specifically cardiovascular and respiratory measures—and the sensor technologies used to capture them, filling a gap in previous reviews that often focused on subjective scales or invasive methods. The authors conducted a systematic review following PRISMA guidelines, searching Science Direct, IEEE Xplore, and MDPI databases for original research published from 2015 onwards. The inclusion criteria were strict: studies had to involve primary data collection in car driving scenarios (simulated or on-road) and focus on cardiovascular (heart rate [HR], heart rate variability [HRV]) and respiratory (respiratory rate [RR]) signals. The review excluded studies involving other modes of transport or those relying primarily on subjective scales like NASA-TLX. Data extracted from selected papers included study type, hardware specifications, test variables, and validation methods. The review also briefly surveys other physiological indicators such as electrodermal activity (EDA) and eye-tracking, noting their limitations in real-world driving due to discomfort or signal noise. The findings highlight that HR and HRV are robust indicators of MWL, derived from the autonomic nervous system’s sympathetic and parasympathetic responses. HRV analysis utilizes time-domain metrics (e.g., SDNN, RMSSD) and frequency-domain power spectral density (VLF, LF, HF bands). Respiratory measures, including tidal volume and minute ventilation, also correlate with cognitive load and vagal control. The review identifies a trend toward non-invasive, contactless sensor technologies for capturing these signals in vehicles, moving away from traditional ECG electrodes that require skin contact. While eye-tracking and EDA are mentioned, the paper argues that cardiovascular and respiratory signals offer a more viable, less intrusive solution for continuous, real-time MWL monitoring in automotive contexts. The significance of this work lies in its comprehensive mapping of the hardware and analytical methods available for in-vehicle physiological monitoring. By detailing the specific sensors and analysis techniques (time/frequency domain) used in recent studies, the review provides a practical framework for automotive engineers and human-factors researchers. It underscores the potential of non-invasive physiological monitoring to enhance driver safety in automated vehicles by objectively detecting cognitive overload or "out-of-loop" states, thereby supporting the development of more responsive and safe human-vehicle interfaces.

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Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• workload measurement
• stress driving
• mental demand
• neuro workload indices
• drowsiness detection algorithms
• cognitive capacity variation

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

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