## 5 Discussion  
The developed methodology is able to close the presented research gap. It gives the basis for performing driving maneuvers to objectify the multidimensional aspects of the driving style of AVs. Since user comfort is an essential part of the driving style of an AV, the methodology also works for user-comfort-related objectification studies.  
Nevertheless, it is indispensable to run through the methodology for every possible interdependence of the driving style's aspects. As a result, a large number of studies are required to define objectification parameters for every possible 2-, 3-, and 4-dimensional interdependence.  
The founded results are one example of the 2-dimensional interdependence between travel time and user comfort. The results show the functionality of the developed methodology for these kinds of problems. Furthermore, the user comfort, which is a subjective perception of every human being, can be correlated to technical parameters.  
The statistical values of the results are in a good range and show their signification. Thus, the presented correlation functions work for the objectification of the interdependence between user comfort and travel time. Statistically, the number of subjects has to be discussed. To reach the final results, it is necessary to conduct a study with subjects of every possible user group to obtain a representative quantity. This was not possible in the present study.  
Furthermore, in literature [53], it is known that the evaluation of the global goodness-of-fit of a nonlinear regression with the R\({}^{2}\) coefficient of determination is statistically problematic. Nevertheless, as no appropriate method exists for a global goodness-of-fit of a nonlinear regression, R\({}^{2}\) is proposed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Maneuver** & **Hypothesis** & **X\({}^{2}\)** \\ \hline \multirow{4}{*}{Maneuver A} & H\({}_{0.1}\) & 36.25 \\  & H\({}_{0.2}\) & 23.86 \\  & H\({}_{0.3}\) & 24.08 \\  & H\({}_{0.4}\) & 26.38 \\ \hline \multirow{4}{*}{Maneuver B} & H\({}_{0.1}\) & 32.92 \\  & H\({}_{0.2}\) & 37.45 \\ \cline{1-1}  & H\({}_{0.3}\) & 30.14 \\ \cline{1-1}  & H\({}_{0.4}\) & 32.78 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results from the X\({}^{2}\)-test for both maneuvers.  
with an additional plausibility check in the literature ([49], p. 40). In the method presented in this paper, the plausibility check is pursued by a visual inspection. In order to be as transparent as possible in the evaluation, all driving data are provided in the Supplementary Materials.  
With the presented methodology, it is only possible to find a suitable maneuver and not the best maneuver. The early selection of a few maneuvers in the pilot study, with only the subjective data of two experts, reduces the effort in the user study but could exclude better maneuvers. Since it is not important to have the best, most detailed test in vehicle concept development, the results could help to develop driving styles of AVs and prove the feasibility of multidimensional driving maneuvers.  
Another point that has to be discussed is the human driver in the study. The subjects may have a different level of trust in a human driver than in the automation. The average correction of the data addresses this. The overall trust in the system does not have any impact. Only the differences between the individual test drives are the input for the correlations.  
The studies were not conducted using an EV. Therefore, the technical setup does not reflect future autonomous EVs. This technical deviation should not falsify the results due to the research focus on correlations between measurable parameters and user comfort. EVs do not have specific user-comfort-affecting characteristics like shift jerking, but the presented correlations are the same for EVs. These results provide a basis for further research on the same or similar problems.