Frederik Naujoks

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.

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 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.

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.

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.

Empirical validation and verification procedures require the sophisticated development of research methodology. Therefore, researchers and practitioners in human–machine interaction and the automotive domain have developed standardized test protocols for user studies. These protocols are used to evaluate human–machine interfaces (HMI) for driver distraction or automated driving. A system or HMI is validated in regard to certain criteria that it can either pass or fail. One important aspect is the number of participants to include in the study and the respective number of potential failures concerning the pass/fail criteria of the test protocol. By applying binomial tests, the present work provides recommendations on how many participants should be included in a user study. It sheds light on the degree to which inferences from a sample with specific pass/fail ratios to a population is permitted. The calculations take into account different sample sizes and different numbers of observations within a sample that fail the criterion of interest. The analyses show that required sample sizes increase to high numbers with a rising degree of controllability that is assumed for a population. The required sample sizes for a specific controllability verification (e.g., 85%) also increase if there are observed cases of fails in regard to the safety criteria. In conclusion, the present work outlines potential sample sizes and valid inferences about populations and the number of observed failures in a user study.

Today, OEMs and suppliers can rely on commonly agreed and standardized test and evaluation methods for in-vehicle human–machine interfaces (HMIs). These have traditionally focused on the context of manually driven vehicles and put the evaluation of minimizing distraction effects and enhancing usability at their core (e.g., AAM guidelines or NHTSA visual-manual distraction guidelines). However, advances in automated driving systems (ADS) have already begun to change the driver’s role from actively driving the vehicle to monitoring the driving situation and being ready to intervene in partially automated driving (SAE L2). Higher levels of vehicle automation will likely only require the driver to act as a fallback ready user in case of system limits and malfunctions (SAE L3) or could even act without any fallback within their operational design domain (SAE L4). During the same trip, different levels of automation might be available to the driver (e.g., L2 in urban environments, L3 on highways). These developments require new test and evaluation methods for ADS, as available test methods cannot be easily transferred and adapted. The shift towards higher levels of vehicle automation has also moved the discussion towards the interaction between automated and non-automated road users using exterior HMIs. This Special Issue includes theoretical papers a well as empirical studies that deal with these new challenges by proposing new and innovative test methods in the evaluation of ADS HMIs in different areas.