Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

Radhakrishnan, Vishnu; Merat, Natasha; Louw, Tyron; R, Goncalves; W, Lyu; Torrão, Guilhermina; Pp, Guillen; Mg, Lenné

doi:10.31234/osf.io/bwp6f

Cited by 1 publication

(2 citation statements)

References 41 publications

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“…Taken the above together, our result may indicate that being closer to the lead vehicle as well as changing the distance to abrupt values away from the mean may be accompanied by higher stress levels and unhealthy states for the driver as well as the passenger. These results are in line with very recent research showing that within a driving simulator, shorter time headways are associated with higher workloads, which might affect drivers' safety [67]. The fact that not only the average distance but also the standard deviation of distance has an impact on drivers' HR highlights the importance of connected vehicles where the distance can be calibrated and kept constant based on different user profiles.…”

Section: Discussionsupporting

confidence: 89%

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How are Drivers' Stress Levels and Emotions Associated with the Driving Context? A Naturalistic Study

Tavakoli¹,

Lai²,

Balali³

et al. 2022

Preprint

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Understanding and mitigating drivers' negative emotions, stress levels, and anxiety is of high importance for decreasing accident rates, enhancing road safety, and providing a healthy lifestyle to the community of drivers. While detecting drivers' stress and negative emotions can significantly help with this goal, understanding what might be associated with increases in drivers' negative emotions and high stress level, might better help with planning interventions. While studies have provided significant insight into detecting drivers' emotions and stress levels; not many studies focused on the reasons behind changes in stress levels and negative emotions. In this study, by using a naturalistic driving study database, we analyze the changes in the driving scene, including road objects and the dynamical relationship between the ego vehicle and the lead vehicle with respect to changes in drivers' psychophysiological metrics (i.e., heart rate (HR) and facial expressions). We find that different road objects might be associated with varying levels of increase in drivers' HR as well as different proportions of negative facial emotions detected through computer vision. Our results indicate that larger vehicles on the road, such as trucks and buses, are associated with the highest amount of increase in drivers' HR as well as negative emotions. Additionally, we provide evidence that shorter distances to the lead vehicle in naturalistic driving, as well as the higher standard deviation in the distance, might be associated with a higher number of abrupt increases in drivers' HR, depicting a possible increase in stress level. Lastly, our results indicate more positive emotions, lower facial engagement, and a lower abrupt increase in HR at a higher speed of driving, which often happens in highway driving. Our results collectively show that driving at higher speeds happening in highways by avoiding certain road objects might be a better fit for keeping drivers in a calmer, more positive state. This research lays the ground for designing human-centered vehicles, urban environments, and services that can understand the level to which each road object and environment might affect drivers' and passengers' wellbeing.

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Section: Discussionsupporting

confidence: 89%

“…For instance, [6] showed that within a simulated platooning scenario, drivers' mental stress measured through biosignals increased as the distance to the lead vehicle decreased. Another very recent study performed in a driving simulator found out that drivers' workload was higher during a shorter time headway [7].…”

Section: Introductionmentioning

confidence: 96%