Predicting Driver Self-Reported Stress by Analyzing the Road Scene

Bustos, Cristina; Elhaouij, Neska; Solé-Ribalta, Albert; Borge-Holthoefer, Javier; Lapedriza, Àgata; Picard, Rosalind W.

doi:10.1109/acii52823.2021.9597438

Cited by 13 publications

(15 citation statements)

References 37 publications

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“…Further, simple and well-organized scenes seem to be related to lower accident rates. The causal plausibility of this uncovered correlation can be supported by extensive literature on attention processes 15,[33][34][35][36] , which confirm that complex scenes are harder to process (in terms of division of attention) by drivers and pedestrians, which could logically lead to an increase in driver's stress 37 or accident rates 10 .…”

Section: Introductionmentioning

confidence: 75%

Predicting the impact of urban change in pedestrian and road safety

Bustos¹,

Rhoads²,

Lapedriza³

et al. 2022

Preprint

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Increased interaction between and among pedestrians and vehicles in the crowded urban environments of today gives rise to a negative side-effect: a growth in traffic accidents, with pedestrians being the most vulnerable elements. Recent work has shown that Convolutional Neural Networks are able to accurately predict accident rates exploiting Street View imagery along urban roads. The promising results point to the plausibility of aided design of safe urban landscapes, for both pedestrians and vehicles. In this paper, by considering historical accident data and Street View images, we detail how to automatically predict the impact (increase or decrease) of urban interventions on accident incidence. The results are positive, rendering an accuracies ranging from 60 to 80%. We additionally provide an interpretability analysis to unveil which specific categories of urban features impact accident rates positively or negatively. Considering the transportation network substrates (sidewalk and road networks) and their demand, we integrate these results to a complex network framework, to estimate the effective impact of urban change on the safety of pedestrians and vehicles. Results show that public authorities may leverage on machine learning tools to prioritize targeted interventions, since our analysis show that limited improvement is obtained with current tools. Further, our findings have a wider application range such as the design of safe urban routes for pedestrians or to the field of driver-assistance technologies.

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Section: Introductionmentioning

confidence: 75%

Predicting the impact of urban change in pedestrian and road safety

Bustos¹,

Rhoads²,

Lapedriza³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…While not in biking studies, similar results were achieved in other transportation studies with respect to other road users such as drivers' stress levels and emotions when getting closer to the intersections. For instance, recent studies both through subjective measures [10], self-reports [18], and increases in HR [58,55] have all shown that drivers experience higher stress level as they arrive at an intersection. However, we note that our findings need to be further verified by future studies due to the limited number and type of intersections in this study.…”

Section: Heart Rate Variationmentioning

confidence: 99%

Roadway Design Matters: Variation in Bicyclists' Psycho-Physiological Responses in Different Urban Roadway Designs

Guo¹,

Tavakoli²,

Robartes³

et al. 2022

Preprint

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As a healthier and more sustainable way of mobility, cycling has been advocated by literature and policy. However, current trends in bicyclist crash fatalities suggest deficiencies in current roadway design in protecting these vulnerable road users. The lack of cycling data is a common challenge for studying bicyclists' safety, behavior, and comfort levels under different design contexts. To understand bicyclists' behavioral and physiological responses in an efficient and safe way, this study uses a bicycle simulator within an immersive virtual environment (IVE). Offthe-shelf sensors are utilized to evaluate bicyclists' cycling performance (speed and lane position) and physiological responses (eye tracking and heart rate (HR)). Participants bike in a simulated virtual environment modeled to scale from a real-world street with a shared bike lane (sharrow) to evaluate how introduction of a bike lane and a protected bike lane with pylons may impact perceptions of safety, as well as behavioral and psycho-physiological responses. Results from 50 participants show that the protected bike lane design received the highest perceived safety rating and exhibited the lowest average cycling speed. Furthermore, both the bike lane and the protected bike lane scenarios show a less dispersed gaze distribution than the as-built sharrow scenario, reflecting a higher gaze focus among bicyclists on the biking task in the bike lane and protected bike lane scenarios, compared to when bicyclists share right of way with vehicles. Additionally, heart rate change point results from the study suggest that creating dedicated zones for bicyclists (bike lanes or protected bike lanes) has the potential to reduce bicyclists' stress levels.

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“…Designing human-centered vehicles require understanding how different factors in and out of the vehicle can affect a user's state, such as stress level, workload, and anxiety [1]. This is mostly due to the fact that drivers' decisionmaking and resulting behaviors are affected by their emotional and cognitive states, which ultimately have severe impacts on driving safety.…”

Section: Introductionmentioning

confidence: 99%

“…In line with addressing this gap, recent research has started to analyze the driving environment together with subjective self-reports of driver's stress to find possible correlations between environmental attributes and driver's stress level. For instance, recent studies found that certain road objects such as bigger vehicles (e.g., trucks), road users (e.g., cyclists), and infrastructural elements (e.g., intersections), as well as in-cabin situations (e.g., working with the center stack), are highly associated with higher subjective stress levels [3], [4], [1]. In addition to road objects, some research studies showed that car-following distance and behavior could also affect drivers' psychological states [5].…”

Section: Introductionmentioning

confidence: 99%

“…We detect abrupt increases by using a change point detector based on Barry and Hartigan's method [18]. We analyze the driving scene retrieved from the video recordings by (1) detecting road objects and (2) estimating the relative distance to the lead vehicle. By analyzing the co-occurrence of the abrupt increases in drivers' HR and the presence of lead vehicles, we find that different road objects might be associated with varying levels of increases in drivers' HR, indicating different stress levels as well as different fractions of negative facial emotions.…”

Section: Introductionmentioning

confidence: 99%

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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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Predicting Driver Self-Reported Stress by Analyzing the Road Scene

Cited by 13 publications

References 37 publications

Predicting the impact of urban change in pedestrian and road safety

Predicting the impact of urban change in pedestrian and road safety

Roadway Design Matters: Variation in Bicyclists' Psycho-Physiological Responses in Different Urban Roadway Designs

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

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