## 2 Materials and Methods
### Measures  
_Number of interactions._ To measure which functions were used and how often during a drive, the number of interactions was counted. The continued and virtually uninterrupted execution of a single task on the checklist was considered one interaction, regardless of task duration. Task execution stopped either as the task was completed or aborted for more than five seconds (see Table 1 for the overview of all tasks). To be able to distinguish whether an interaction pertained to the same or to a different task, a cooldown timer was used. It was set to five seconds as it was assumed that, when a driver performs the same task again within five seconds or continues with a task within five seconds, it is still part of the same task, in which case it was counted as a single interaction. However, turning on a function was considered a separate task from turning the same function off. Counting began when the driver moved the vehicle for the first time. Only interactions with the original installed HMI equipment were part of the observations. Each interaction was counted separately.  
Based on the counts for the separate tasks and the trip durations, the individual task frequencies and total frequencies per category were calculated.  
_Driving context._ To be able to account for contextual factors during the drive, which may influence the use frequency of specific functions, the following factors were logged: road type, outside temperature, weather type, peak hour and day of the week. Similar to [PERSON] et al. [17], the following road types were distinguished: city roads, rural roads and motorways. In addition, it was also logged when the participant was in a traffic jam, either in standstill or driving. To calculate the frequencies per road type, the driving time per road type was documented.  
Weather type and outside temperature were included as they could influence the use of climate-control functions and windshield wipers. For example, it was hypothesised that rainy weather increases the number of tasks related to the windshield. Peak hour and day of the week were included, as [PERSON] et al. [18] found that traffic density had an impact on secondary task engagement.  
_Socio-demographic variables._ To understand whether or not socio-demographic variables contribute to function usage, the following factors were registered: age, gender and driving experience. Older drivers (55-75 years) have been found to struggle more with the use of IVIS while driving, compared to younger drivers between 21 and 36 years of age [19]. Other research has shown that novice drivers engage in distracting tasks more frequently as they gain more driving experience [20]. Drivers are also more inclined to engage in secondary tasks when task difficulty is low, which may be the case for more experienced drivers [12]. In the current study, driving experience therefore included both participants' driving frequency as well as the time they had held their driving licence.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Vehicle Characteristics** & \begin{tabular}{l} **Availability in the** \\ **Familiar Cars (_n_ = 30)** \\ \end{tabular} & \
\begin{tabular}{l} **Availability in the Unfamiliar Car** \\ **(Seat Toledo 2014)** \\ \end{tabular} \\ \hline Adaptive climate control & 63.3\% & Yes \\ \hline Adaptive cruise control & 36.7\% & No \\ \hline Automatic lights & 70.0\% & No \\ \hline Transmission &
\begin{tabular}{l} Manual (56.7\%), \\ Automatic (43.3\%) \\ \end{tabular} & Manual \\ \hline Touch screen & 66.7\% & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the availability of particular functions and some of the car’s characteristics for both the familiar cars as well as for the unfamiliar car.