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

Area of Science:

• Automotive Engineering
• Human Factors
• Cognitive Science

Background:

• Conditional automated vehicles require drivers to monitor driving and be ready to resume control.
• Fluctuations in driving demand can significantly alter a driver's mental workload (MWL).
• Altered MWL may impair a driver's ability to effectively take over vehicle control.

Approach:

• Literature review focusing on in-vehicle physiological sensors for MWL analysis.
• Emphasis on cardiovascular and respiratory measures as indicators of MWL.
• Analysis of study types, hardware, analytical methods, and results.

Key Points:

• Physiological indices, particularly cardiovascular and respiratory measures, offer objective insights into driver MWL.
• Understanding MWL is crucial for ensuring safe transitions of control in automated vehicles.
• Various physiological sensors and analysis techniques are employed in automotive MWL research.

Conclusions:

• Physiological monitoring provides a viable method for assessing driver MWL in automated driving scenarios.
• Further research is needed to refine the use of physiological data for real-time MWL assessment and safety interventions.
• This review synthesizes current knowledge, identifying gaps and future directions in using physiological measures for automotive MWL analysis.