## 4 Discussion and Conclusions
As a result, there is considerable variety in the exact driving circumstances for the participants, which increases the noise in the data. The advantage of the method used is thatwe were able to observe driving behaviour under rather naturalistic circumstances since, in the real-world, most trips are made along familiar roads [12; 13; 30; 31; 32]. To somewhat account for the lack of control over the routes and still be able to understand the driving contexts, certain characteristics of the route and the weather conditions were noted. Due to choosing their own familiar routes, the exact trip length also varied between participants, though the total trip time never exceeded one hour. This time budget was chosen as it was expected that participants might not have been willing to drive longer than 60 min. In addition, these trip durations have resulted in distances travelled that are reasonably close to the average distance per car trip in the Netherlands, which is found to be 17.44 km [33]. The ride-along method of simply counting the number of interactions has been shown to be effective, though there are also some drawbacks. Counting was performed manually, which makes it more vulnerable to human error. Some people also found it difficult to refrain from engaging in a conversation with the researcher. This could be remedied by using cameras, which would also allow for tracking people for longer periods of time and/or using a larger participant sample. Nevertheless, for the purpose of the current study, the ride-along method has proven to be efficient, as it is an easy-to-use, low-budget method. Another limitation of the current study is that the Seat Toledo that was used as the unfamiliar car lacks some modern features. This may have increased the number of interactions with the unfamiliar vehicle, as less functions could be put on auto mode. Nevertheless, the results of this study are particularly relevant for vehicle HMI designers as well as for policymakers. They may also prove to be a useful input for HMI guidelines and assessment tools. For example, the UDV decision tree [8] includes use frequency of a function to determine the appropriate function implementation. This decision tree is one of the main building blocks of Euro NCAP's new assessment protocol on HMIs for 2026, to encourage car manufacturers to implement good HMI practices and facilitate the safe use of general vehicle controls [26]. For this, their protocol assesses a wide variety of functions used while driving. Insights into which functions are used significantly while driving compared to which are less frequently used help to understand which functions should be easily and swiftly available while driving in order to reduce distraction by the car's HMI design. Conceptualization, D.A.M.A., I.M.H. and M.P.H. ; methodology, D.A.M.A., I.M.H., E.P. and M.P.H. ; formal analysis, D.A.M.A., E.P. and I.M.H. ; investigation, D.A.M.A. ; data curation, D.A.M.A. ; writing--original draft preparation, I.M.H. and D.A.M.A. ; writing--review and editing, E.P. and M.P.H. ; supervision, I.M.H., E.P. and M.P.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted in accordance with the Declaration of Helsinki and approved by the Human Research Ethics Committee (HREC) of the TU Delft (application number 3679, 16 November 2023). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The supplementary data supporting the conclusions of this article is available through doi: 10.4121/280897 cc-6302-41a5-b330-17f8e376c680. The authors thank RDW (The Netherlands Vehicle Authority) for providing the unfamiliar vehicle. The authors declare no conflicts of interest.