# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
## 5 Conclusions  
The results of other studies indicate that both consumers and car salespeople are insufficiently informed about Advanced Driver Assistance Systems (ADASs). The majority of car salespeople acquire their knowledge through trial and error or training provided by the vehicle manufacturer or an intermediary. This paper presents a summary of the results for the study group. The data were analysed in terms of the type of training received and the age of participants. The maximum possible score was 5. The results show that practical training was the most effective, with a mean score of 4.0 and a median of 4.25. In comparison, the e-learning format had a mean score of 3.11, while the brief manual instruction yielded a mean score of 3.01. Furthermore, the analysis of skill acquisition between test scenarios revealed an average improvement ratio of 1.74, demonstrating that the training provided was effective in fostering learning. The significance of these findings lies in providing quantitative evidence that interactive, hands-on training is critical for ensuring drivers can safely operate an ADAS, which is a key factor for enhancing overall road safety in an era of increasing vehicle automation. The outcomes were also compared with the trainer evaluation provided after the completion of the driving session. Furthermore, a model was developed to assess driver behaviour, separately considering the results from individual scenarios. For the majority of participants, an upward trend in scores across subsequent scenarios was observed, indicating progress and skills acquisition. The objective assessment carried out using the model also highlighted the limitations of subjective evaluations. Although, in most cases, trainer assessments were aligned with those assigned by the model, in many instances, the trainer's evaluation showed discrepancies with the patterns observed in the objective data and the actual course of the training. To investigate the causes of significant discrepancies, trainer notes from the questionnaires were reviewed. This analysis revealed that trainers often considered not only the ability to operate the system but also additional factors. Variations in evaluations were also evident depending on the trainer, with some being more demanding and others more lenient. Moreover, in several cases, the trainer's score was significantly lower than that assigned by the model. These discrepancies stemmed from observations that were only possible for a human observer. The primary novelty of this work lies not in the finding that practical training is superior but in the development and validation of a transparent, data-driven methodology to objectively demonstrate it. The proposed fuzzy logic model offers a significant contribution by providing a standardised and reproducible alternative to traditional, purely subjective trainer assessments. By grounding the evaluation in objective performance data, the model overcomes the inherent biases and variability of human judgment. This methodological innovation provides a robust tool for certifying driver competence and comparing the effectiveness of different training programmes, ultimately supporting the wider goal of enhancing road safety in an era of increasing vehicle automation. The findings presentedin this article illustrate the multitude of factors influencing subjective evaluation. Trainer assessments often depended not only on system operation skills or reaction time to a signal, but an additional challenge was the inability of trainers--without proper tools such as a stopwatch--to precisely determine the reaction time. The results underscore the importance of using appropriately designed models that leverage objective data. Such approaches can contribute to more reliable assessments of training participants. Nevertheless, trainer comments also highlight scenarios in which subjective evaluation appears indispensable. These include cases and situations that only a human observer can detect.