Objectives. The aim of this study was to create a Czech translation of the Perceived Stress Scale (PSS), to assess its psychometric properties on a representative sample of the Czech general adult population, and to compare the original 14-item version (PSS-14) with the shortened 10-item (PSS-10) and four-item (PSS-4) versions. Sample and setting. Two pilot studies were conducted to create the final Czech translation of the scale (n = 365 and n = 420). The final version of the Czech PSS was administered to a sample of the Czech general adult population (n = 1725 of whom 981 were women, M = 44.32, SD = 12.8). The Beck Depression Inventory (BDI-II) or the State-Trait Anxiety Inventory (STAI) were administered alongside the PSS to a part of the sample. A retest measurement after 14 days was conducted (n = 159). Statistical analysis. Using the confirmatory factor analysis, the one-factor, two-factor and bifactor models were compared. The internal consistency, stability in time, and convergent validity of the scale, as well as the known-group differences, were assessed. The three versions of the PSS were compared. Results. The confirmatory factor analysis supported the bifactor model of the PSS-14 and PSS-10, and the two-factor model of the PSS-4. All versions of the scale showed good internal consistency and stability in time. There was a moderate to strong positive correlation between the PSS and the BDI-II and STAI. Differences based on age, sex, education level, and situational factors were found. Overall, the PSS-10 showed the best psychometric properties of all three versions of the scale. Study limitation. The sample consisted mostly of highly educated respondents.
Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study evaluates ambient in-vehicle lighting that continuously communicates the current level of AV reliability, specifically on how it could influence drivers' take-over performance and mental workload (MW). We conducted an experiment in a driving simulator with 42 participants who experienced 10 take-over requests (TORs). The experimental group experienced a four-stage ambient light display that communicated the current level of AV reliability, which was not provided to the control group. The experimental group demonstrated better take-over performance, based on lower vehicle jerks. Notably, perceived MW did not differ between the groups, and the EEG indices of MW (frontal theta power, parietal alpha power, Task-Load Index) did not differ between the groups. These findings suggest that communicating the current level of reliability using ambient light might help drivers be better prepared for TORs and perform better without increasing their MW.
Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study evaluates ambient in-vehicle lighting that continuously communicates the current level of AV reliability, specifically on how it could influence drivers’ take-over performance and mental workload (MW). We conducted an experiment in a driving simulator with 42 participants who experienced 10 take-over requests (TORs). The experimental group experienced a four-stage ambient light display that communicated the current level of AV reliability, which was not provided to the control group. The experimental group demonstrated better take-over performance, based on lower vehicle jerks. Notably, perceived MW did not differ between the groups, and the EEG indices of MW (frontal theta power, parietal alpha power, Task–Load Index) did not differ between the groups. These findings suggest that communicating the current level of reliability using ambient light might help drivers be better prepared for TORs and perform better without increasing their MW.
Background. Driving simulators allow studying driving behaviour under controlled settings. To reproduce the driver’s behaviour as realistically as possible, we need reliable driving simulators which allow participants to get highly immersed. The property of the technological system delivering the experience is a crucial dimension of immersion. Understanding the effect of different simulator settings on brain activity is vital to ensure that future research in neuroergonomics is reproducible and consistent. This study explores the impact of system immersion on the drivers’ brain activity when operating a conditionally automated vehicle in a driving simulator task.Method. Two groups of participants operated an autonomous vehicle for four sequential 10-minute-long rides while conducting a non-driving secondary task. The high-immersion group (nine participants) operated a fixed-base driving simulator with a 190 ° field of view. The low-immersion group (10 participants) operated the same simulator, but only the front screen was used. Two participants from the high-immersion group were excluded due to simulator sickness. We recorded the brain activity using a 32-channel EEG headset.Results. We used the Mann-Whitney U test to compare the relative mean power over the whole ride in the Theta, Alpha, Beta, low-Beta, and high-Beta bandwidth. We found a significant difference between the groups in the Beta bandwidth (12-30 Hz) at the Cz position; and the high-Beta bandwidth (22-30 Hz) at the Oz, O2, P3, P8, and Cz position. Using mixed ANOVA, we assessed the effect of time (four sequential rides) and group (high- and low-immersion) on relative mean power in the frontal, parietal, occipital, and temporal regions. For the Alpha bandwidth, the two-way mixed ANOVA revealed a significant effect of time in the parietal, temporal, and frontal regions. Neither the group nor the interaction effects were found in other bandwidths. Implementing the Bayesian approach, we found strong evidence against the effect of group on frontal, temporal, and parietal Alpha. Moreover, we found moderate evidence against the effect of time on frontal and parietal Theta, occipital Beta, and occipital high-Beta. We also found moderate or strong evidence against the interaction of time and group on frontal and parietal Theta; occipital, temporal, and parietal Beta; and occipital and parietal high-Beta. Finally, the Bayesian approach suggested equal support for H0 and HA for the effect of group on parietal Beta and parietal high-Beta.Conclusion. Our results suggest that the system immersion of a driving simulator might affect the oscillatory brain activity, especially in the high-beta bandwidth and in the parietal and occipital areas. This finding complies with previous studies on immersion. Moreover, our results indicate that the high-immersion settings could increase the involvement; however, it could also be more stressful. This seems relevant, especially in the light of the increased simulator sickness in the high-immersion group. The Bayesian approach suggests that more data would be needed to draw final conclusions.
Drivers' role changes with increasing automation from the primary driver to a system supervisor. This study investigates how supervising an SAE L2 and L3 automated vehicle (AV) affects drivers' mental workload and sleepiness compared to manual driving. Using an AV prototype on a test track, the oscillatory brain activity of 23 adult participants was recorded during L2, L3, and manual driving. Results showed decreased mental workload and increased sleepiness in L3 drives compared to L2 and manual drives, indicated by self-report scales and changes in the frontal alpha and theta power spectral density. These findings suggest that fatigue and mental underload are significant issues in L3 driving and should be considered when designing future AV interfaces.
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