Objective The goal of this review is to investigate the relationship between indirect physiological measurements and direct measures of situation awareness (SA). Background Assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SA global assessment technique (SAGAT), situation present assessment method (SPAM), and/or SA rating technique (SART). However, research suggests that physiological sensing methods may also be capable of inferring SA. Method Seven databases were searched. Eligibility criteria included human–subject experiments that used at least one direct SA assessment technique as well as at least one physiological measurement. Information extracted from each article were the physiological metric(s), direct SA measurement(s), correlation between these two metrics, and experimental task(s). Results Twenty-five articles were included in this review. Eye tracking techniques were the most commonly used physiological measures, and correlations between conscious aspects of eye movement measures and direct SA scores were observed. Evidence for cardiovascular predictors of SA was mixed. Only three electroencephalography (EEG) studies were identified, and their results suggest that EEG was sensitive to changes in SA. Overall, medium correlations were observed among the studies that reported a correlation coefficient between physiological and direct SA measures. Conclusion Reviewed studies observed relationships between a wide range of physiological measurements and direct assessments of SA. However, further investigations are needed to methodically collect more evidence. Application This review provides researchers and practitioners a summary of observed methods to indirectly assess SA with sensors and highlights research gaps to be addressed in future work.

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Significant growth in the number of autonomous vehicles is expected in the coming years. With this technology, drivers will likely begin to disengage from the driving task and often experience mind wandering. Research has examined the effects of mind wandering on manual driving performance, but little work has been done to understand its impact on autonomous driving. In addition, it is unclear what physiological measurements can reveal about mind wandering in the driving context. Therefore, the goals of this paper were to (a) understand how mind wandering affects warning signal detection, semi-autonomous driving performance, and physiological responses, and (b) develop a model to predict mind wandering. Preliminary findings suggest that mind wandering may be observed as a result of road familiarity, and that the number of driving years and response times to alerts may be suitable predictors of mind wandering. This work is expected to help inform the design of future autonomous vehicles to prevent distracted driving behaviors.

Automated Driving Systems (ADS) have become significantly more advanced over the past decade. Currently, intermediate levels of automation, i.e., SAE Level 3 that control most of the driving tasks (SAE, 2021), often require intervention and/or shared control from human drivers. This means that drivers need to have a sufficient understanding of the driving environment. However, visibility conditions, particularly nighttime driving, can make vehicle-to-driver takeover tasks considerably difficult.The use of conditional vehicle automation is also likely to encourage drivers to disengage from the driving task and engage in non-driving-related secondary tasks (NDRTs) (Large et al., 2017), even when they are aware that a takeover may be needed (Wandtner et al., 2018). Especially at night, drivers tend to confine their visual attention to only the illuminated areas (Brimley et al., 2014), which can moderate how drivers voluntarily allocate their visual attention between NDRTs and the external roadway. Several studies have examined the effects of engagement in NDRTs on takeover performance, but most have done so for daytime driving (McDonald et. al., 2019). Little is known about the influence of external (low) lighting conditions on takeover performance.The objective of this study was to compare the effects of engagement in different visual NDRTs on takeover performance during nighttime. This study did not directly compare daytime and nighttime takeover performance. Instead, it assessed various takeover performance measures after an unexpected takeover request (TOR), when drivers voluntarily engaged in an NDRT that required visual attention to be located on versus off the road.