Driving in an urban environment, the stress response and effects of exercise

Event-related driver stress detection with smartphones in an urban environment: a naturalistic driving study

Driving in urban areas can be challenging and encounter acute stress. To detect driver stress, collecting data on real roads without interfering the driver is preferred. A smartphone-based data collection protocol was developed to support a naturalistic driving study. Sixty-one participants drove on predetermined real road routes, and driving information as well as physiological, psychological, and facial data were collected. The algorithm identified potentially stressful events based on the collected data. Participants classified these events as low, medium, or highly stressful events by watching recorded videos after the experiment. These events were then used to train prediction models. The best model achieved an accuracy of 92.5% in classifying low/medium/highly stressful events. The contribution of physiological, psychological, and facial expression indices and individual profile information was evaluated. The method can be applied to visualise the geographical distribution of stressors, monitor driver behaviour, and help drivers regulate their driving habits.

Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet

OBJECTIVE

Owing to the harsh environment in high-altitude areas, drivers experience significant driving stress. Compared with urban roads or expressways in low-altitude areas, the driving environment in high-altitude areas has distinct features, including mountainous environments and a higher proportion of trucks and buses. This study aims to investigate the feasibility of predicting stress levels through elements in the driving environment.

METHODS

Naturalistic driving tests were conducted on an expressway in Tibet. Driving stress was assessed using heart rate variability (HRV)-based indicators and classified using K-means clustering. A DeepLabv3 model was built to conduct semantic segmentation and extract environment elements from the driving scenarios recorded through a camera next to the driver's eyes. A decision tree and 4 other ensemble learning models based on decision trees were built to predict driving stress levels using the environment elements.

RESULTS

Fifty-six indicators were extracted from the driving environment. Results of the prediction models demonstrate that extreme gradient boosting has the best overall performance with the F1 score (harmonic mean of the precision and recall) and G-mean (geometric mean of sensitivity and specificity) reaching 0.855 and 0.890, respectively. Indicators based on the variation rate of trucks and buses have high feature importance and exhibit positive effects on driving stress. Indicators reflecting the proportion of mountain, road, and sky features negatively affect the expected levels of driving stress. Additionally, the mountain feature demonstrates multidimensional effects, because driving stress is positively affected by indicators of the variation rate for mountain elements.

CONCLUSIONS

This study validates the prediction of driving stress using environment elements in the driver's field of view and extends its application to high-altitude expressways with distinct environmental characteristics. This method provides a real-time, less intrusive, and safer method of driving stress assessment and prediction and also enhances the understanding of the environmental determinants of driving stress. The results hold promising applications, including the development of a driving state assessment and warning module as well as the identification of high-risk road sections and implementation of control measures.

Evaluation of driver stress intervention with guided breathing and positive comments

While many methods have been proposed to detect driver stress with high accuracy, few studies have explored how to mitigate stress during driving effectively. This study proposed and evaluated two driver stress intervention methods, i.e., auditory Positive Comments w/o haptic Breathing guidance (BPC and PC). Sixty drivers were randomly assigned to four groups (i.e., no stress, stressful but no intervention-NI, PC, and BPC) and completed a simulated driving task with their physiological, psychological, and behavioral data collected. Driver stress was effectively induced by challenging simulated driving events. Haptic guidance provided by smartwatches efficiently regulated the breathing rate to the target. Engaging in the intervention was associated with increased RMSSD and did not worsen driving performance. Participants perceived moderate to large comfort effects. The complexity of driving scenarios should be considered for choosing interventions. Breathing intervention was less effective when complex maneuvers were required than normal driving. The findings provided implications regarding the design of in-vehicle stress intervention systems for intelligent transportation.

Implementation and evaluation of a fuzzy-based system for determining stress feeling level in VANETs: Effect of driving experience and history on driver stress

Drivers are held responsible for the vast majority of traffic accidents. While most of the errors that cause these accidents are involuntary, a significant number of them are caused by irresponsible driving behaviors, which must be utterly preventable. Irresponsible driving, in addition, is often associated with the stress drivers experience while driving. We have previously implemented an intelligent system based on fuzzy logic for determining driver’s stress in Vehicular Ad hoc Networks (VANETs), called Fuzzy-based System for Determining the Stress Feeling Level (FSDSFL), considering the driver’s impatience, the behavior of other drivers, and the traffic condition as input parameters. In this work, we present an Improved FSDSFL (IFSDSFL) system, which considers the driving experience and history as an additional input. We show through simulations the effect that driving experience and history and the other parameters have on the determination of the stress feeling level and demonstrate some actions that can be performed when the stress exceeds certain levels.

A Fuzzy Logic Approach for Determining Driver Impatience and Stress Leveraging Internet of Vehicles Infrastructure

Drivers are held responsible for the vast majority of traffic crashes. Although most of the errors causing these accidents are involuntary, a significant number of them are caused by irresponsible driving behaviors, which must be utterly preventable. Irresponsible driving, on the other hand, is often associated with driver stress and the impatience they show while driving. In this paper, we consider the factors that cause drivers to become impatient and experience stress and propose an integrated fuzzy logic system that determines the stress level in real time. Based on the stress level, the proposed system can take the appropriate action that improves the driving situation and consequently road safety. By using inputs, such as the unnecessary maneuvers that drivers make, the time pressure, and the number of times they are forced to stop, a fuzzy logic controller determines the driver’s impatience, which is then considered alongside other factors, such as the driving experience and history, the behavior of other drivers, and the traffic condition to determine the stress level. We show, through simulations, the feasibility of the proposed approach to accurately determine driver stress and demonstrate some actions that can be performed when stress exceeds certain levels.

Detecting driver stress and hazard anticipation using real-time cardiac measurement: A simulator study

OBJECTIVES

In the context of growing interest in real-time driver stress detection systems, we question the value of using heart rate change over short time periods to detect driver stress and hazard anticipation.

METHODS

To this end, we explored changes in heart rate and speed as well as perceived stress in 27 drivers in a driving simulator. Driver stress was triggered by using hazardous road events, while hazard anticipation was manipulated using three levels of hazard predictability: unpredictable (U), predictable (P), and predictable and familiar (PF).

RESULTS

The main results indicate that using heart rate change (1) is a good indicator for detecting driver stress in real time, (2) provides a cardiac signature of hazard anticipation, and (3) was affected by perceived stress groups. Further investigation is needed to validate the lack of relationship between increased anticipation/predictability and strengthened cardiac signature.

CONCLUSIONS

These results support the use of heart rate change as an indicator of real-time driver stress and hazard anticipation.

Sensitivity of Physiological Measures of Acute Driver Stress: A Meta-Analytic Review

Background: The link between driving performance impairment and driver stress is well-established. Identifying and understanding driver stress is therefore of major interest in terms of safety. Although many studies have examined various physiological measures to identify driver stress, none of these has as yet been definitively confirmed as offering definitive all-round validity in practice. Aims: Based on the data available in the literature, our main goal was to provide a quantitative assessment of the sensitivity of the physiological measures used to identify driver stress. The secondary goal was to assess the influence of individual factors (i.e., characteristics of the driver) and ambient factors (i.e., characteristics of the context) on driver stress. Age and gender were investigated as individual factors. Ambient factors were considered through the experimental apparatus (real-road vs. driving simulator), automation driving (manual driving vs. fully autonomous driving) and stressor exposure duration (short vs. long-term). Method: Nine meta-analyses were conducted to quantify the changes in each physiological measure during high-stress vs. low-stress driving. Meta-regressions and subgroup analyses were performed to assess the moderating effect of individual and ambient factors on driver stress. Results: Changes in stress responses suggest that several measures are sensitive to levels of driver stress, including heart rate, R-R intervals (RRI) and pupil diameter. No influence of individual and ambient factors was observed for heart rate. Applications and Perspective: These results provide an initial guide to researchers and practitioners when selecting physiological measures for quantifying driver stress. Based on the results, it is recommended that future research and practice use (i) multiple physiological measures, (ii) a triangulation-based methodology (combination of measurement modalities), and (iii) a multifactorial approach (analysis of the interaction of stressors and moderators).

Cardiovascular response to whole-body vibration on an automobile seat

The study aim was to determine whether a relationship exists between the cardiovascular response, measured by HR and HRV and the magnitude of whole-body vibration. Cardiovascular response of sixty male participants in four groups, was measured during three states i.e. (1) no vibration, (2) a reference vibration and (3) an alternative vibration. The reference vibration was the same for all groups with the alternative vibrations different for each group. Weighted vertical seat vibration was 0.66 m.s^-2, root-mean-square for the reference and 0.70, 0.73, 0.76, and 0.79 m.s^-2, root-mean-square for the alternative vibrations. Vibrations only differed in magnitude with the difference between alternative vibrations based on relative difference thresholds. Nonparametric tests compared cardiovascular indicators between groups at State 3 adjusted for state of departure i.e. State 2. No significant differences between groups were found for most of the indicators, suggesting no relationship between cardiovascular response and the magnitude of whole-body vibration. Practitioner summary: The cardiovascular response to the magnitude of whole-body vibration on an automobile seat was investigated. Results suggest that no relationship exists between the magnitude and cardiovascular response and that the latter may not be as effective as other objective measures (e.g. acceleration) in evaluating the human's response to whole-body vibration.

Predicting Driver Stress Levels with a Sensor-Equipped Steering Wheel and a Quality-Aware Heart Rate Measurement Algorithm

Unobtrusive monitoring of driver mental states has been regarded as an important element in improving the safety of existing transportation systems. While many solutions exist relying on camera-based systems for e.g., drowsiness detection, these can be sensitive to varying lighting conditions and to driver facial accessories, such as eye/sunglasses. In this work, we evaluate the use of physiological signals derived from sensors embedded directly into the steering wheel. In particular, we are interested in monitoring driver stress levels. To achieve this goal, we first propose a modulation spectral signal representation to reliably extract electrocardiogram (ECG) signals from the steering wheel sensors, thus allowing for heart rate and heart rate variability features to be computed. When input to a simple logistic regression classifier, we show that up to 72% accuracy can be achieved when discriminating between stressful and non-stressful driving conditions. In particular, the proposed modulation spectral signal representation allows for direct quality assessment of the obtained heart rate information, thus can provide additional intelligence to autonomous driver monitoring systems.

The short-term effects of artificially-impaired binocular vision on driving performance

Appropriate visual function is paramount to ensuring adequate driving performance and road safety. Here, we examined the influence of sudden artificially-impaired binocular vision on driving performance using a car simulator. Twenty-four young drivers (mean age 22.42 ± 3.19 years) drove under three different visual conditions (natural driving, monocular blur, and monocular occlusion) through three different traffic environments with low, medium, and high levels of complexity (highway, rural, and city, respectively). We assessed their driving performance, perceived level of task complexity, and subjectively-experienced road safety. Furthermore, as a manipulation check, we also evaluated the drivers' cardiac vagal responses, as a well-known index of task complexity. The sudden deterioration of binocular vision caused unsafe driving behaviours (distance out of the road and maximum breaking intensity) in the most complex traffic environments. Specific self-regulatory strategies (i.e. increased cardiac vagal responses) and subjective responses corroborated these results. Practitioner summary: This study provides evidence that the sudden deterioration of binocular vision has a detrimental effect on simulated driving performance. Our analysis of cardiovascular functioning shows that drivers adopt self-regulatory strategies when their binocular vision functioning is compromised. Abbreviations: VA: visual acuity; BV: binocular vision; HRV: heart rate variability; NASA: TLX: NASA-Task Load Index; SSS: Stanford Sleepiness scale; RMSSD: root mean square of successive difference; HF: high-frequency.

Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study

Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.