# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
###### Abstract  
This paper evaluates the effectiveness of driver training programmes aimed at the proper use of Advanced Driver Assistance Systems (ADASs). Participants (N = 49) were divided into the following three groups based on the type of training received: practical training, e-learning, and brief manual instruction. The effectiveness of the training methods was assessed using selected parameters obtained from driving simulator studies, including reaction times and system activation attempts. Given the large volume and nonlinear nature of the input data, a heuristic, expert-based approach was used to identify key evaluation criteria, structure the decision-making process, and define fuzzy rule sets and membership functions. This phase served as the foundation for the development of a fuzzy logic model in the MATLAB environment. The model processes inputs to generate a quantitative performance score. The results indicate that practical training (mean score = 4.0) demonstrates superior effectiveness compared to e-learning (3.09) and manual instruction (mean score = 3.01). The primary contribution of this work is a transparent, data-driven evaluation tool that overcomes the inherent subjectivity and bias of traditional trainer-based assessments. This model provides a standardised and reproducible approach for assessing driver competence, offering a significant advancement over purely qualitative, trainer-based assessments and supporting the development of more reliable certification processes. driver training; advanced driver assistance systems; driving simulators 2020  
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Despite substantial technological progress in vehicle systems, driver training methodologies have not evolved accordingly. While established models of driver learning exist, modern curricula have largely failed to integrate systematic instruction on Advanced Driver Assistance Systems (ADASs) [19; 20]. In the absence of formal training, drivers are left to learn these systems through trial and error [21; 22; 23]. At present, there is a lack of reliable educational and informational materials on the proper use of ADASs. Research conducted by the Motor Transport Institute shows that most drivers either do not know how to use these systems or learn to operate them while driving via trial and error. Only 27% of respondents reported acquiring knowledge from the vehicle's user manual [24]. As part of Task 1 of the AV-PL-ROAD project [25], an in-depth analysis of manuals revealed that they range from 400 to 900 pages in length and are often so complex that they discourage thorough reading. Additionally, translation errors and ambiguous language frequently hinder comprehension [26]. Marketing and promotional materials often misrepresent the actual performance and capabilities of these systems, leading to an overreliance and a dangerous sense of security and distraction while driving. For this reason, it appears essential to develop innovative methods for training drivers in the use of systems already implemented in current vehicles. Providing reliable information in an accessible format could significantly increase the knowledge of current and prospective drivers, directly impacting traffic safety [27]. Many current driving courses address at least some psychomotor, perceptual, and cognitive skills, which have been shown to be associated with increased crash risk among young drivers. Unfortunately, due to the wide range of topics covered and limited training time, these aspects are often addressed only superficially. From a safety perspective, training effectiveness could be enhanced by emphasising collision avoidance skills through hazard detection and accurate risk assessment. These competencies should be developed through practice in high-risk scenarios that would be too dangerous to replicate in real traffic conditions. Driving simulators provide a suitable platform for such training [28; 29; 30; 31; 32; 33].