## 8 Conclusions  
The presented analysis showed the high effectiveness of the developed proprietary model. Basing the performance assessment on objective data results in a high match of the assessment to the actual level of the driver's skills. The advantage of the method is the reduction in the human factor and issuing more adequate assessments based on real data. Even if clear evaluation criteria are introduced, subjective feelings will always vary slightly depending on the evaluator and their aptitude.  
The results presented in the paper show how many factors affect subjective assessment. The ratings given by the trainers often did not depend solely on the ability to operate the system or the response time to a given signal. An additional difficulty was the inability to determine the exact value of reaction time by a coach who was not even equipped with a stopwatch.  
However, there are situations that can only be properly interpreted by a human observer. Therefore, the most advantageous method of assessment would be to use a combination of the objective method presented in this study with subjective verification carried out by the trainer. The model can therefore be used as an instrument to improve the evaluation process, provided that the subjective component is governed by precisely defined criteria to ensure reliability and consistency.  
However, it should be remembered that in the case of using subjective assessment, even when using the commonly used scale of 1-5, it is necessary to define precise assessment criteria and ensure that each of the assessed persons is subject to the same requirements. The use of a scale containing a detailed description will increase the reliability of the grade.  
While primarily focused on training assessment, this study's findings offer salient implications for Advanced Driver Assistance Systems (ADASs) design. The objective metrics derived from our evaluation model, particularly those related to driver activation attempts (number_of_samples), reaction times to takeover requests (time_reaction_Rtl), and full control handover (response_time_wl_system), can directly inform the refinement of Human-Machine Interfaces (HMIs). Such refinements should aim to enhance communication clarity and the seamlessness of control transitions, especially by providing unambiguous system status feedback. Ultimately, these insights can contribute to the development of more intuitive ADAS, fostering safer and more effective human-machine collaboration, and the model itself may serve as a benchmark for future ADAS interface development.  
Conceptualization, M.P. and M.K.; methodology, M.P. and M.K.; software, M.P.; validation, M.P., M.K. and Z.K.; formal analysis, M.P. and M.K.; investigation, M.P. and M.K.; resources, M.P. and M.K.; data curation, M.P. and M.K.; writing--original draft preparation, M.P. and M.K.; writing--review and editing, M.P., M.K. and Z.K.; visualization, M.P.; supervision, M.P. and M.R.; project administration, M.P. and M.K.; funding acquisition, Z.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Motor Transport Institute in Warsaw (Resolution number 3.2025 on 3 June 2025).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.