## 7 Conclusions  
In this article, we have reviewed the status quo of methods utilized in human factors research in driving automation. We followed a structured approach to give an overview of the research domain by selecting relevant papers, and reviewing them in a standardized manner using a relational database. There is a good portion of research in different aspects of driving automation indicating that researchers in the community work on the issue of developing and improving human-machine interfaces for automated vehicles. When researchers plan to engage in research and development of automated driving, the present work provides them with an overview of the current landscape. Thus, one can derive information about main research areas and emerging trends that have not been studied extensively yet. Additionally, this literature review provides researchers and practitioners with suggestions about methodological tools (i.e., collection methods and specific parameters) that they can use when assessing a certain construct of a driving automation system. To conclude, we list a set of recommendations to be considered in future experiments addressing automated driving:* **Existing study results obtained in driving simulators should be validated in realistic on-road settings. ** As most experimental results were obtained in driving simulators (or used even lower degree of realism/immersion), their main findings must urgently be validated in more realistic settings, especially when addressing constructs that incorporate risk (such as trust in automation). * **In contrast to single session experiments, insights into the long-lasting effects of AD usage are scarce and can benefit from longitudinal study designs. ** Another huge potential for future work are longitudinal studies. Such studies cannot only validate results obtained in single-session experiments: they might even reveal new issues which have not been addressed yet. * **Depending on the particular research question, we suggest going beyond established constructs like safety, trust, acceptance, or workload. ** In addition, take constructs into account that have not yet been intensively addressed (such as personalization, cooperation, wellbeing, etc.). Cooperation in particular refers to different parties performing as a team together rather than only one party at a time [87]. When designing HMIs for vehicles, consider the full spectrum of user experience research and include user satisfaction (such as hedonic qualities) in HMI evaluation also as these aspects will finally decide between success or defeat of the technology on the market. * **To increase validity, the sample characteristics should be adapted to the addressed research questions. In addition, researchers need to explain why the particular sample was chosen and considered valid. ** Aim for a participant sample that allows for investigating the proposed research question plausibly. Do not only list a more diverse sample as a limitation, but also discuss how your results could be affected by biases in participant sampling. * **Researchers should consider the collection of more than a single source of data (i.e., triangulating behavioral, psychophysiological, and self-reported data). Insights from self-report measures might be worthy of future research for its effects on user behavior. ** Evaluation of different types of data sometimes leads to contradicting results. Such conflicts should not be avoided, as in the best case they contribute to a better understanding of established theory. Only comprehensive evaluation of the involved factors allows for drawing meaningful conclusions. * **Consider existing or proposed standards for measurements to allow for comparison of study results. ** The possibility to compare study results is a key element of scientific practices. Thus, if possible, utilize standardized methods (regarding parameters, their measurement times, as well as their calculation).