## 1 Introduction
These methods often employ adaptive techniques that can adjust to environmental changes in real time, as well as reinforcement learning and optimization algorithms to identify and manage critical scenarios. Data-driven approaches and high-fidelity simulations also play a central role, ensuring that the widest range of relevant scenarios is examined during testing [19; 20; 21; 22; 23; 24]. Based on the reviewed literature, it is apparent that although there are several well-utilizable approaches for both the selection of test cases and the proper definition of the ODD, none really address how to compile a collection of test cases tailored to a given autonomous function or vehicle with a relatively small number of already accepted test cases. Such a collection would allow for a high level of certainty in determining the safety level of the vehicle in critical cases, which could facilitate the efficiency of pre-deployment inspections aimed at testing purposes. In the everyday work of a TIC company, alongside the detailed and extensive testing campaigns aimed at discovering any misbehavior and conducting thorough inspections, it is also crucial to assess the vehicles before public road testing or deployment. This assessment ensures that we identify any potential issues early on. Therefore, a more generic method must be developed to provide a clear picture of the vehicles' readiness and general safety. In this article, we present a method that allows for the definition and selection of test cases for an autonomous vehicle to test safety-critical situations in a test track environment. Our primary objective was to develop a test planning methodology based on existing regulatory-based scenarios. Although there are currently few standardized or regulatory test cases specifically designed for autonomous vehicles, many ADAS functions share significant similarities with automated driving features--both technologically and in terms of test conditions. Therefore, we also used established ADAS test cases as a starting point. Our intention was to work with industry-accepted scenarios rather than generate entirely new ones, which would require extensive parameterization and result in a large number of test cases--making the process both time-consuming and costly. In our work, we also define a set of preliminary input parameters gathered through a questionnaire, which allows us to select the appropriate cases for the given vehicle to conduct the aforementioned tests. The method developed in this way can be particularly well utilized for pre-deployment safety checks before releasing road vehicles for either operational or testing purposes in traffic, for which different authorities and TIC companies currently do not have harmonized, effective solutions. Since such test plans are often created in an ad hoc manner, relying heavily on engineering intuition and experience, we aimed to provide a more systematic and scientifically grounded selection process. This motivation led to the development of the methodology presented in our paper. The structure of this work is as follows: In the Section 2, the developed methodology is presented, including the creation of a comprehensive test run database and the development and use of a structured questionnaire. In the Section 3, the applicability of the method is demonstrated using real-world projects, and the alignment of the selected scenarios with previous test plans is analyzed. Finally, in the Section 4, the findings are critically evaluated in the context of existing studies, and recommendations for future research and development are presented.