# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
## 4 Results  
The results of the analysis are presented in two phases. First, the outcomes of the preliminary fuzzy logic model are introduced, wherein overall driver performance was assessed through the aggregation of data across all test scenarios. Although this approach offers a general performance overview (Figure 7), it exhibits notable methodological limitations--specifically, its inability to account for the learning progression observed by trainers and an excessive sensitivity to early-stage errors. These constraints prompted the development of a refined, second iteration of the model, which constitutes the basis for the subsequent section on model validation. The weakest outcomes were recorded among participants who underwent the brief manual instruction. As many as 50% of them received an unacceptable rating. Additionally, the model's results indicate that 4 out of 16 individuals (25%) from the brief manual instruction group obtained a score below 2. When analysing the data for the entire sample, it should be noted that as many as 33 participants received an acceptable or very good rating. A comparison of the results across age groups (Table 2) indicates that, regardless of age, the most effective form of training was practical instruction conducted by an instructor. This approach enabled a thorough familiarisation with the material and the opportunity to practice individual tasks with the support of a qualified professional. Following practical training, only two participants in the younger driver group received a score below 4.0. Among participants aged 40 to 65, five out of nine individuals achieved a score above 4.4. Only one person in this age group received a score below 3. To statistically validate the observed differences among the training groups, a series of non-parametric tests were conducted, as the data did not meet the assumptions for a standard ANOVA. A Kruskal-Wallis H test was performed to determine if there were statistically significant differences in key performance metrics across the three training types (practical, e-learning, and manual instruction). The analysis revealed several significant findings based on the data collected during the test scenarios. For the total driver reaction time to a takeover request, a statistically significant difference was found among the groups, H(2) = 10.58, \(p\) = 0.005. Post hoc pairwise comparisons indicated that participants in the practical training group reacted significantly faster than those who received only manual instruction (\(p\) = 0.006) and those in the e-learning group (\(p\) = 0.004). The difference in reaction times between the e-learning and manual instruction groups was not statistically significant (\(p\) > 0.05). Furthermore, a significant difference was observed in the number of attempts required to correctly activate the ADAS in the first test scenario, H(2) = 8.27, \(p\) = 0.016. Post hoc analysis showed that the practical training group required significantly fewer attempts compared with both the manual instruction group (\(p\) = 0.016) and the e-learning group (\(p\) = 0.010). Similarly, the difference between the e-learning and manual instruction groups was not statistically significant (\(p\) > 0.05). These inferential tests provide robust statistical evidence supporting our primary hypothesis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age Group** & **Training Type** & **Group Mean** & **Standard Deviation** \\ \hline \multirow{3}{*}{Age group 27–39} & practical & 4.11 & 0.83 \\  & e-learning & 3.09 & 0.87 \\  & manual & 3.65 & 0.99 \\ \hline \multirow{3}{*}{Age group 40–65} & practical & 3.91 & 0.92 \\  & e-learning & 3.14 & 0.38 \\ \cline{1-1}  & manual & 2.19 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of outcomes for individual age groups depending on the training received. Figure 7: Comparison of the results according to the type of training received, based on the initial aggregated-data model.