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’ 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.

The responsibility and role of human drivers during automated driving might change dynamically. In such cases, human-machine interface (HMI) transparency becomes crucial to facilitate driving safety, as the states of the automated vehicle have to be communicated correctly and efficiently. However, there is no standardized transparency assessment method to evaluate the understanding of human drivers toward the HMI. In this study, we defined functional transparency (FT) and, based on this definition, proposed a transparency assessment method as a preliminary step toward the objective measurement for HMI understanding. The proposed method was verified in an online survey where HMIs of different vehicle manufacturers were adopted and their transparencies assessed. Even though no significant result was found among HMI designs, FT was found to be significantly higher for participants more experienced with SAE Level 2 automated vehicles, suggesting that more experienced users understand the HMIs better. Further identification tests revealed that more icons in BMW’s and VW’s HMI designs were correctly used to evaluate the state of longitudinal and lateral control. This study provides a novel method for assessing transparency and minimizing confusion during automated driving, which could greatly assist the HMI design process in the future.

Manipulating MW in driving simulator studies without the need to introduce a non-driving-related task remains challenging. This study aims to empirically evaluate the modified speed regulation n-back task, a tool to manipulate drivers’ MW. Our experiment involved 23 participants who experienced a 0-back and 2-back driving condition, with task-irrelevant novel environmental sounds used to elicit P3a event-related potentials. Results indicate that the 2-back condition was perceived as more demanding, evidenced by higher NASA-TLX scores (overall score, mental and temporal demand, effort, frustration). The mean P3a amplitude was diminished during the 2-back condition compared to the 0-back condition, suggesting that drivers experienced higher MW and had fewer resources available to process the novel environmental sounds. This study provides empirical evidence indicating that the speed regulation n-back task could be a valid, effective, and reproducible method to manipulate MW in driving research.