Physiological Sensing for Driver State Monitoring: Technology Scan and Pilot Evaluation

Abstract

As advanced driver assistance systems and automated vehicle technologies evolve, the ability to monitor and assess driver readiness remains critical. In support of that need, this report describes several efforts to evaluate the feasibility and reliability of capturing physiological signals during real-world driving. The goal was to examine whether signals from respiration, cardiac activity, and brain function, captured via wearable and non-contact sensors, could complement existing driver monitoring methods and provide useful input for future in-vehicle systems. A review of the literature supporting respiration, cardiac, and brain activity as relevant domains for driver state monitoring was the initial step in this process. These physiological channels are discussed as potentially useful complements to traditional measures like eye behavior, especially given their links to autonomic and cognitive changes under various degraded states. The literature review was complemented by a technology scan of commercial and research-grade devices capable of measuring these signals in mobile contexts. A structured protocol for exercising an initial subset of these systems during impaired driving in a closed-course environment was also developed. This protocol was then executed in a pilot test with five participants, who completed standardized baseline and post-alcohol drives while instrumented with electrocardiogram (ECG), respiration, and electroencephalography (EEG) sensors in addition to sensors capturing driving performance and glance behavior. In this pilot study, alcohol was used as a convenient physiological stressor given its well-understood dosage effects on driving and physiology. Results indicated that alcohol consumption consistently altered some behavioral physiological signals across all three domains. Physiological responses were more robust and consistent than the observable driving metrics, potentially highlighting the complementary value of these signals. The small sample size, however, resulted in a lack of power to detect statistical significance between sober and impaired driving for most metrics. Respiration and ECG signals were captured with high reliability, while EEG results provided informative patterns but suffered from variable signal quality and motion artifacts. These findings support the initial viability of cardiac and respiratory sensing in mobile in-vehicle settings and highlight practical limitations that must be addressed for EEG. Altogether, the effort demonstrates that it is technically feasible to capture and interpret physiological signals in a real-world driving context using wearable and embedded sensors. While further validation is needed, the results provide a foundation for integrating such signals into future driver state monitoring systems, not as standalone indicators, but as part of a multimodal approach that reflects the complexity of driver physiology and behavior.