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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.

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.

Within a workshop on evaluation methods for automated vehicles (AVs) at the Driving Assessment 2019 symposium in Santa Fe; New Mexico, a heuristic evaluation methodology that aims at supporting the development of human–machine interfaces (HMIs) for AVs was presented. The goal of the workshop was to bring together members of the human factors community to discuss the method and to further promote the development of HMI guidelines and assessment methods for the design of HMIs of automated driving systems (ADSs). The workshop included hands-on experience of rented series production partially automated vehicles, the application of the heuristic assessment method using a checklist, and intensive discussions about possible revisions of the checklist and the method itself. The aim of the paper is to summarize the results of the workshop, which will be used to further improve the checklist method and make the process available to the scientific community. The participants all had previous experience in HMI design of driver assistance systems, as well as development and evaluation methods. They brought valuable ideas into the discussion with regard to the overall value of the tool against the background of the intended application, concrete improvements of the checklist (e.g., categorization of items; checklist items that are currently perceived as missing or redundant in the checklist), when in the design process the tool should be applied, and improvements for the usability of the checklist.

Research on the role of non-driving related tasks (NDRT) in the area of automated driving is indispensable. At the same time, the construct mode awareness has received considerable interest in regard to human–machine interface (HMI) evaluation. Based on the expectation that HMI design and practice with different levels of driving automation influence NDRT engagement, a driving simulator study was conducted. In a 2 × 5 (automation level x block) design, N = 49 participants completed several transitions of control. They were told that they could engage in an NDRT if they felt safe and comfortable to do so. The NDRT was the Surrogate Reference Task (SuRT) as a representative of a wide range of visual–manual NDRTs. Engagement (i.e., number of inputs on the NDRT interface) was assessed at the onset of a respective episode of automated driving (i.e., after transition) and during ongoing automation (i.e., before subsequent transition). Results revealed that over time, NDRT engagement increased during both L2 and L3 automation until stable engagement at the third block. This trend was observed for both onset and ongoing NDRT engagement. The overall engagement level and the increase in engagement are significantly stronger for L3 automation compared to L2 automation. These results outline the potential of NDRT engagement as an online non-intrusive measure for mode awareness. Moreover, repeated interaction is necessary until users are familiar with the automated system and its HMI to engage in NDRTs. These results provide researchers and practitioners with indications about users’ minimum degree of familiarity with driving automation and HMIs for mode awareness testing.

Automated driving systems (ADS) and a combination of these with advanced driver assistance systems (ADAS) will soon be available to a large consumer population. Apart from testing automated driving features and human–machine interfaces (HMI), the development and evaluation of training for interacting with driving automation has been largely neglected. The present work outlines the conceptual development of two possible approaches of user education which are the owner’s manual and an interactive tutorial. These approaches are investigated by comparing them to a baseline consisting of generic information about the system function. Using a between-subjects design, N = 24 participants complete one training prior to interacting with the ADS HMI in a driving simulator. Results show that both the owner’s manual and an interactive tutorial led to an increased understanding of driving automation systems as well as an increased interaction performance. This work contributes to method development for the evaluation of ADS by proposing two alternative approaches of user education and their implications for both application in realistic settings and HMI testing.