# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
## 3 Training Evaluation Model  
This study presents an evaluation of the effectiveness of the training methods based on selected parameters collected during simulator studies. Due to the large volume of input  
Figure 3: An exemplary research situation [40]. data and their nonlinear nature, a heuristic approach was proposed for the evaluation. A fuzzy logic model was developed in the MATLAB environment (Version R2023a, The MathWorks, Inc., Natick, MA, USA) using the Fuzzy Logic Designer tool [42]. This software allows for the implementation of two types of inference systems, as follows: Mamdani and Sugeno. The Mamdani-type system was selected for the analysis due to its ease of use in systems where rules are derived from expert knowledge. In this model, the control system is built by synthesising a set of rules obtained from experienced human operators. The output of each rule is a fuzzy set [42]. Statistical analysis of the simulator data revealed significant differences in three evaluated parameters depending on the type of training undertaken. These parameters included the following:  
* The total reaction time from the moment of the initial auditory prompt to activate the system to the removal of feet from the pedals and hands from the steering wheel;
* The number of attempts to activate the system;
* The driver's reaction time to a request for takeover. Accordingly, the developed model includes the following three inputs:  
* number_of_samples--The total number of attempts to activate the system in the test scenario;
* time_reaction_Rtl--Reaction time to the request for takeover (in seconds);
* response_time_wl_system--Total reaction time from the prompt to the removing of feet from the pedals and hands from the steering wheel (full relinquishment of control) (in seconds). One output, Behaviour, indicates the assessment of the driver's behaviour during the scenario. The block diagram of the developed model is presented in Figure 4.  
In the subsequent step, membership functions were defined for all inputs, as well as for the output of the controller. The parameters of the Gaussian curves for each variable were determined through a multi-stage process. This process was primarily based on the analysis of data collected by the authors in previous studies using the Highway Chauffeur system on the same simulator. These initial ranges were then verified and supplemented by a review of the relevant literature on driver reaction times in simulated environments. Membership functions were specified for each of the input variables. For the input variable number_of_attempts, three membership functions were defined, as follows: optimal, acceptable, and excessive. To ensure methodological transparency and reproducibility, these categories were quantified based on precise numerical thresholds defined per single research situation. An optimal performance corresponded to a single attempt (1 try). The acceptable category was defined as requiring a maximum of two attempts. A performance was classified as excessive when a participant needed three or  
Figure 4: Model of the fuzzy controller. more attempts to complete the task. This quantitative framework corresponds directly to the observed participant behaviour; an optimal number of attempts indicated that the participant successfully activated the system on the first try, demonstrating full proficiency. An acceptable number of attempts meant that the participant required an additional try but was still able to complete the task effectively. An excessive number of attempts signified that the participant was unsure how to proceed, potentially deactivating the system unintentionally and, thus, had to restart it multiple times. For the input describing the total driver reaction time to the system activation prompt (see Figure 5), three membership functions were defined, as follows: short, average, and long. A short reaction time indicated a prompt driver response; average suggested that the driver did not respond immediately, but the reaction time remained within an acceptable range.