In-vehicle voice assistants (VAs) can potentially enhance driving safety and user experience. However, their success depends on trust and acceptance. This study explores two influencing factors: (1) design-based and (2) performance-based attributes. In a driving simulator study, N = 53 participants engaged with VAs varying in anthropomorphic appearance and response latency. Results indicate that neither factor universally enhanced trust or acceptance. Instead, a nuanced interaction emerged: When response latency was short, anthropomorphic appearance had no impact. However, when response latency was long, both the non- and high-anthropomorphic VA were rated as more trustworthy than a low-anthropomorphic VA. Additionally, participants rated the high-anthropomorphic VA equally trustworthy across both latencies, suggesting a potential buffering effect of anthropomorphism under suboptimal performance conditions. Furthermore, trust fully mediated the relationship between the factors and acceptance, supporting models that conceptualize trust as a precursor of acceptance. Based on these insights, design recommendations were derived.

Driver monitoring systems (DMS) represent a camera-based countermeasure for visual distraction that detect distracted drivers in real-time and subsequently prompt them to look back on the road. However, the effectiveness of DMS in reducing distraction is still being debated, with studies yielding inconsistent results. A correct understanding of a technological system is a key determinant for its effectiveness in terms of enhancing safety. Therefore, addressing a previously unexplored factor in DMS research, this study investigated how drivers' explicit knowledge about DMS influences system effectiveness in reducing visual distraction. Previous studies showed that drivers' understanding of DMS is incomplete if drivers are not instructed. Therefore, in this study, the drivers' explicit knowledge of DMS was systematically manipulated between participants by providing verbal instructions prior to driving. Three experimental conditions were compared: drivers with explicit knowledge of DMS, drivers without knowledge, and a control group with an inactive DMS (between factor). Glance behavior was compared between the first and repeated interaction with the secondary task to assess visual distraction (within factor). Explicit knowledge significantly reduced the number of glances exceeding 2 s, indicating reduced visual distraction. This effect was prevalent, even after repeated interaction with the DMS. Importantly, mere activation of DMS without instruction did not affect glance behavior. Findings highlight the significant role of explicit knowledge in system effectiveness. The present work contributes to the field of DMS research by investigating drivers' mental model of DMS and deriving methodological and practical implications.

Touchscreens that offer various functions to the driver have become an integral part of the user interface in vehicles. To enable the use of these functions while preventing driver distraction, there is a trend towards implementing driver monitoring systems (DMS). DMS detect distraction and warn drivers accordingly. However, there is limited empirical evidence on the effectiveness of DMS in preventing visual distraction. Furthermore, potential interactions with contextual factors like secondary task complexity and task experience are neglected so far. Therefore, the present study investigated the effectiveness of a DMS, based on the Euro NCAP protocol, that issued warnings when long distraction (>= 3 s) or visual attention time sharing (>= 10 s in a 30 s window) occurred while driving on a highway. Glance behavior of 57 participants was analyzed while performing secondary tasks on an in-vehicle display with varying levels of task complexity and task experience. The effectiveness of the DMS depended on the task complexity and task experience. For more complex tasks, the DMS significantly reduced the number and total duration of glances to the display, but only for inexperienced trials. For experienced trials, no effect of the DMS was found. For less complex tasks, the DMS significantly reduced the duration of single glances and the proportion of long glances at the display, regardless of task experience. The results indicate that the effectiveness of DMS in reducing distraction is not as straightforward as assumed, emphasizing the importance of evaluating DMS in the context of contextual factors to draw accurate conclusions.

As in-vehicle voice assistants (IVVAs) become integral to modern driving experiences globally, it is essential to understand how their design impacts user experience (UX) across different cultures. Therefore, this study explores the interaction effects of visual appearance, system performance, and cultural background on both pragmatic and hedonic qualities of UX. In a driving simulator study, N = 105 participants from China and Germany interacted with IVVAs featuring varying levels of anthropomorphism (non-, low, high anthropomorphic) and response latency (short, long). The results revealed a universal preference for a short response latency. However, the impact of anthropomorphism on UX appears to be culturally dependent: While anthropomorphic design enhanced the hedonic qualities of UX for both Chinese and German participants, its impact on pragmatic UX qualities showed variation depending on cultural background. These findings underscore the necessity of adopting a user-centered, culturally-informed approach to IVVA design to optimize UX across diverse markets.

The Affinity for Technology Interaction (ATI) scale has been widely used to assess the tendency to engage in technology. To enhance the scale’s applicability and facilitate cross-cultural research, it is essential to provide translations of the scale. A Chinese translation is still missing. Additionally, a validation is necessary as culture can affect the psychometrics of questionnaires, bearing the danger of applying inadequate measures. The aims of the present study are therefore providing a Chinese translation of the ATI scale and presenting non-parametric and parametric psychometric analyses of the translated version to examine the underlying factor structure. In contrast to the original scale, analyses of the Chinese version suggest a two-dimensional structure with one dimension describing a passive interest in technology and another describing an active engagement with technology. The findings enable researchers to use the scale in Chinese-speaking populations and thereby advancing the understanding of human-technology interaction across different cultures.

Multimodal in-vehicle infotainment systems offer drivers a range of non-driving-related functions but can increase visual-manual and cognitive task demand, compromising road safety. Therefore, it is important to estimate the secondary task demand of these systems early in the development process. To do so, the Box Task combined with a Detection Response Task (BT + DRT) was developed as a straightforward laboratory method. The BT is used to quantify the visual-manual task demand, while the DRT is capable of assessing cognitive demand. However, previous studies showed that difficult cognitive secondary task demand led to a similar decrease in performance in the BT to that found in the easy visual-manual demand. Therefore, this study aimed to enhance the BT’s sensitivity to visual-manual demand and discriminability from cognitive demand by increasing the difficulty of the BT. Additionally, the effects of increased BT difficulty on DRT metrics, self-assessed mental workload and secondary task performance were examined. In total, N = 39 participants performed the BT + DRT with varying BT difficulty levels (easy, moderate and difficult), secondary task types (visual-manual vs. cognitive) and secondary task difficulty levels (easy vs. difficult). The results indicated that lateral variability at moderate and difficult BT levels was the BT metric with the largest and most consistent effect sizes for assessing visual-manual secondary task demand and to discriminate from performance impairments resulting from cognitive task demand. At both BT levels, the DRT is also capable of effectively assessing cognitive demand, either through response time or the number of omissions. For self-assessed workload, only slight increases in ratings were observed for higher BT difficulty levels. There were only minor changes in secondary task performance, such as slightly slower responses, during more difficult BT levels. Consequently, a higher BT difficulty than previously used is recommended for the BT + DRT paradigm.

In a driving simulator study, six experts interacted with an in-vehicle voice assistant (VA) and rated different latencies. The results suggest that in order to maintain drivers’ satisfaction with the interaction, in-vehicle VAs should have a latency of no more than 5 s. A slight delay of 1.5 s was rated best while shorter latencies caused highest variance amongst experts—indicating that the fastest response may not necessarily be the most desirable. Satisfaction may also depend on the complexity of the use case as experts showed higher tolerance toward longer latencies in the navigation-domain. Furthermore, our results raise thought-provoking insights about the importance of considering human expectations and preferences in the design of in-vehicle VAs. Despite technological advancements, humans might still expect a natural delay, similar to that in a human-human interaction. These findings emphasize the need to balance using cutting-edge technology with the desire for familiar interactions.

Anthropomorphism in product marketing is particularly prevalent in Eastern versus Western marketplaces, potentially driven by a cultural preference. This trend may extend to the automotive domain, where leading Chinese car manufacturers have already adopted anthropomorphic designs for intelligent personal assistants (IPAs). This paper examines cross-cultural differences in the prevalence of anthropomorphic IPAs, aiming to determine if a disproportionality exists between global markets. The study also explores underlying cultural dimensions (i.e., collectivism, power distance, uncertainty avoidance) that may account for these observed differences. The findings reveal a notably higher prevalence of anthropomorphic IPAs in vehicles by Chinese manufacturers compared to global counterparts. Correlational analyses suggest specific cultural patterns may contribute to the differing prevalence across markets. However, further user studies are necessary to ascertain whether Chinese consumers in fact respond more favorably to anthropomorphic IPAs. This exploration offers first insights that can inform the adoption of anthropomorphic in-vehicle IPAs.

The proposed test procedure presents an approach for the evaluation of the usability of partial automated driving HMI including driver monitoring systems in driving simulation. This procedure is based on a definition of requirements that a Level 2 HMI and its included driver monitoring system must fulfill in order to guarantee that the drivers understand their responsibilities of continuously monitoring the driving environment and the status of the partial automated driving system. These requirements are used to define the evaluation criteria that have to be validated in the test as well as the use cases in which these criteria can be assessed. The result is a detailed and comprehensive test guide including the specification of the test drives, the necessary instructions, the test environment and the recruiting criteria for the test sample. • Evaluation of usability aspects of level 2 automated driving HMI including driver monitoring systems • Based on the definition of requirements for L2 HMI • Test guide including the definition of use cases, evaluation criteria and testing conditions in driving simulation

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The present study investigated the effects of a driver monitoring system that triggers attention warnings in case distraction is detected. Based on the EuroNCAP protocol, distraction could either be long glances away from the forward roadway (≥3s) or visual attention time sharing (>10 cumulative seconds within a 30 s time interval). In a series of manual driving simulator drives, 30 participants completed both driving related tasks (e.g., changing multiple lanes in dense traffic) and non-driving related tasks (e.g., infotainment operations). Results of warning frequencies revealed that visual attention time sharing warnings occurred more frequently than long distraction warnings. Moreover, there was a large number of attention warnings during driving related tasks. Results also revealed that participants' mental models tended to be less accurate when it came to understanding of the visual attention time sharing warnings as compared to the long distraction warnings, which were understood more accurately. Based on these observations, the work discusses the applicability and design of driver monitoring warnings.

The design of automotive human–machine interfaces (HMIs) for global consumers’ needs to cater to a broad spectrum of drivers. This paper comprises benchmark studies and explores how users from international markets—Germany, China, and the United States—engage with the same automotive HMI. In real driving scenarios, N = 301 participants (premium vehicle owners) completed several tasks using different interaction modalities. The multi-method approach included both self-report measures to assess preference and satisfaction through well-established questionnaires and observational measures, namely experimenter ratings, to capture interaction performance. We observed a trend towards lower preference ratings in the Chinese sample. Further, interaction performance differed across the user groups, with self-reported preference not consistently aligning with observed performance. This dissociation accentuates the importance of integrating both measures in user studies. By employing benchmark data, we provide insights into varied market-based perspectives on automotive HMIs. The findings highlight the necessity for a nuanced approach to HMI design that considers diverse user preferences and interaction patterns.

The Box Task combined with a Detection Response Task (BT + DRT) is a relatively new and easy-to-use method to assess in-vehicle system demand, consisting of a visual-manual task (BT) and a cognitive task (tactile DRT). Currently, little is known regarding the sensitivity of the BT + DRT for different types and difficulty levels of secondary tasks. Therefore, the present study evaluated the BT + DRT's sensitivity compared to the Lane Change Test (LCT), which is an ISO-standardized test method. Fifty-two participants engaged in a visual-manual (Surrogate Reference Task) and a cognitive secondary task (counting task) across two levels of difficulty while performing a PC version of the BT + DRT and LCT. The BT parameters, especially the standard deviation of box position and size, were sensitive to visual-manual secondary task demand. Moreover, the results showed that the DRT is a sensitive method to assess cognitive demand, supporting previous findings. In contrast, the mean deviation of the adaptive reference lane used as the standard LCT parameter was not as effective in discriminating between visual-manual and cognitive tasks. Hence, to distinguish between different types and levels of secondary task demand, the BT + DRT is more accurate than the standard LCT version. Future studies should investigate how an increased BT difficulty can further improve the sensitivity to visual-manual and cognitive secondary task demand.

In the presented driving simulator study, we propose a new test protocol for the assessment of the distraction potential of head-up display (HUD) technologies in the driving context. The method combines driving-related measures with the visual detection response task (DRT) as common evaluation protocols using eye glance measurement are no longer valid. The protocol was applied comparing several use cases in two conditions: a head-down display (HDD) and a head-mounted HUD using smart glasses. The results revealed that in relation to a reference task (manual radio tuning), the smart glasses did not impair either driving performance or visual workload significantly. Additionally, they led to lower visual workload than the HDD when reading text messages, but not when performing simpler tasks. The study points towards a positive effect on the distraction potential of visual-manual tasks with head-mounted HUDs and includes a first proposal for a standardised distraction assessment for these technologies.

The Box Task combined with a Detection Response Task (BT + DRT) is a relatively less investigated but promising method for evaluating visual-manual and cognitive task demand due to the interaction with in-vehicle information systems while driving. The BT includes the tracking of a dynamic box whose size and position follow a sinusoidal pattern with uniform amplitudes and frequencies. However, it is unclear whether participants are able to predict and adapt to these uniform dynamics, which might lead to a reduced sensitivity of the BT + DRT. Within the present study, it was aimed to examine differences in BT + DRT performance depending on uniform and non-uniform BT dynamics. A laboratory study was conducted with N = 41 participants. The experimental conditions differed in the type and difficulty level of the secondary tasks as well as in the BT dynamics (uniform, varying amplitude, varying frequency). While the uniform BT dynamics could be more predictable, the non-uniform BT dynamics were designed slightly easier in their difficulty using a lower frequency or amplitude. The results revealed no performance benefits when performing uniform BT dynamics compared to non-uniform BT dynamics. The frequency BT condition was related to a significantly lower variability of box position and higher gaze duration on the secondary task compared to the uniform BT dynamics. These findings suggest that participants are not or only negligible able to adapt to the uniform BT dynamics. Therefore, it is recommended to use the uniform BT dynamics as suggested and implemented in previous studies.

Speech-based interfaces can be a promising alternative and/or addition to visual-manual interfaces since they reduce visual-manual distraction while driving. However, there are also findings indicating that speech-based assistants may be a source of cognitive distraction. The aim of this experiment was to quantify drivers' cognitive distraction while interacting with speech-based assistants. Therefore, 31 participants performed a simulated driving task and a detection response task (DRT). Concurrently they either sent text-messages via speech-based assistants (Siri, Google Assistant, or Alexa) or completed an arithmetic task (OSPAN). In a multifactorial approach, following Strayer et al. (2017), cognitive distraction was then assessed through performance in the DRT, the driving speed, the task completion time and self-report measures. The cognitive distraction associated with speech-based assistants was compared to the OSPAN task and a baseline condition without a secondary task. Participants reacted faster and more accurately to the DRT in the baseline condition compared to the speech conditions. The performance in the speech conditions was significantly better than in the OSPAN task. However, driving speed did not significantly differ between the experimental conditions. Results from the NASA-TLX indicate that speech-based tasks were more demanding than the baseline but less demanding than the OSPAN task. The task completion times revealed significant differences between speech-based assistants. Sending messages took longest with the Google Assistant. Referring to the findings by Strayer et al. (2017), we conclude that nowadays speech-based assistants are associated with a rather moderate than high level of cognitive distraction. Nonetheless, we point towards the need to assess the effects of human-machine interaction via speech-based interfaces due to their potential for cognitive distraction.

Driver monitoring systems detect driver distraction and will become a standard safety feature in vehicles soon. Advanced driver distraction warnings prompt drivers through different modalities to keep their attention on the road. With added driving automation, research suggests that drivers tend to be distracted and take their eyes off the road more often. To increase safety, distraction warnings will be used in conjunction with driving automation. However, there is no empirical evidence on how drivers’ altered gaze behavior affects the occurrence of distraction warnings when adding automation. In the present driving simulator study, N=22 participants performed use cases that triggered distraction warnings while driving in different automation modes. Results showed that warnings were most frequently triggered by long glances away from the roadway. Furthermore, it became evident that the warning frequency differed, depending on the automation mode. Finally, future research on advanced driver distraction warnings is outlined.

Various methods have been developed to measure user experience (UX). The majority of measures considers the multidimensionality of the construct including the User Experience Questionnaire (UEQ). However, the UEQ neglects that the individual UX-components might be of unequal importance to different cultures. Therefore, the aim of this contribution was to gain first insights on what users from different countries, i.e., China, Germany, and the United States, cherish in an automotive user interface (UI) using the six factors provided by the UEQ. The results indicated that participants from different cultural backgrounds preferred different UI-qualities. Based on the findings, we compiled some initial thoughts on how to consider cultural differences both in the design of an automotive UI as well as in the methodological approach. We raise the question whether a weighting scheme for the UEQ is worth considering in cross-cultural UX-research.

Augmented reality (AR) technology could establish direct relationships between displayed information and objects in the real driving environment, e.g. by highlighting relevant objects in the traffic environment. However, it is unclear how these potential benefits of augmentation affect drivers' distraction from the driving task and their level of workload. Since gaze detection cannot separate glances at AR-elements from glances at the driving scene, new evaluation criteria are needed for measuring the distraction potential of AR-elements. The paper evaluates a methodology based on the Detection Response Task (DRT) for measuring the distraction and workload effects of three different display technologies: Head-Down Display (HDD), Head-Up Display (HUD), and Augmented-Reality HUD (AR-HUD). The DRT measures cognitive and visual workload based on a recognition-response task using the two parameters, reaction time and misses. Two experimental studies using driving simulation (N = 24) and production vehicles in real traffic (N = 24) are reported. As use cases, the subjects had to navigate through an urban traffic environment and pass different intersections. During that they were assisted by navigation systems based on the above mentioned display technologies. The resulting workload is compared to a baseline drive without the need to navigate and a reference drive in which the subjects had to work on a secondary task while driving. The results of the DRT are compared with subjective measures, gaze behaviour, and driving performance. In addition, drivers' preference and user experience regarding the three display technologies and their feedback concerning the DRT method were assessed. The results of both experimental studies show that drivers prefer the AR-HUD display technology. Reasons are mainly the hedonic aspects of the AR-HUD such as the novelty of the technology. Nevertheless, drivers report that AR-elements could obstruct important areas of the road and distract more from the driving task than HUDs. The subjective and visual-cognitive workload measured by the DRT do not differ between the HMI conditions. An analysis for each type of intersection yields most misses for the AR-HUD condition and fewest misses in the HDD condition. However, visual workload (i.e. eyes-off-road time) is higher for HDD compared to (AR-)HUD. Feedback by participants reveals that the DRT as a method for workload assessment is well accepted. All in all, the two DRT measures, misses and reaction time, allow a valid and objective workload measurement and could serve as a supplement or substitute for gaze analysis. The discussion gives indications for the measurement of workload effects caused by AR-HUD using DRT.

Bidirectional charging management could offer benefits to individuals, society and energy providers by using the batteries of Battery Electric Vehicles (BEVs) as a means of storage. To successfully design and implement this technology, it is necessary to match customer mobility needs with the goals of the energy sector. Therefore, practice and research need a better understanding of potential customers’ acceptance of the new technology. The authors address this void by providing a review of the existing user-centered literature on users’ perceptions and demands regarding smart charging. In addition, the authors conducted qualitative interviews to gain up-to-date, country- and use case-specific insights. The authors interviewed experienced BEV drivers as well as owners of photovoltaic systems and presented them with two use cases: Intraday and Optimized Private Consumption. The results give an overview about facilitating factors as well as potential barriers to the adaptation of bidirectional charging (BC). However, the review shows that research focusing on actual users of the technology is still rare. In addition, the interviews reveal that perceptions and demands differ between scenarios, which should be addressed by future research.