## 2 Materials and Methods
### Review and Evaluation of Current Test Scenarios to Develop a Comprehensive Test Run Database  
To create an appropriate test scenario catalog tailored to a given application, it is first necessary to gather the correct input data. For this purpose, a questionnaire was developed, which enabled a good approximation to filter for the appropriate test scenarios. Creating the questionnaire required a review of the current regulations, standards, and test protocols dealing with ADAS and autonomous vehicle testing, as well as ODD descriptions. Consequently, as an initial step in our work, we reviewed the available regulations that are in effect or soon to be enforced concerning ADAS features or autonomous vehicles, along with other decisive testing procedures, such as EuroNCAP protocols and ISO standards related to active safety (Table 1). The goal was to gain a clearer picture of how the technology-driven working groups developing these regulations approach the problem. From the existing regulations, protocols, and standards, we gathered all the test runs per test scenarios, resulting in a list comprising more than 1000 test runs differing in parameters. We also collected the key parameters of the different runs into a database, which plays an important role in the selection of test cases. Processing the ADAS testing procedures was deemed especially important because many of them could be highly relevant to the testing of autonomous vehicles from a technological and vehicle dynamics perspective; hence, they definitely provide a good starting point in creating the planned scenario collection. The collected parameters can be grouped into major categories based on their functionalities. Some parameters relate to the initial movement of the vehicle, such as speed, maneuver, and direction. Others pertain to the targets used (e.g., pedestrian, bicycle, car dummies), including the speed, movement, and direction of the target; overlap ratio; and target type. Additional parameters describe the specific movement of the vehicle during a test run, such as lateral speed or acceleration, or driver behavior during distraction. Figure 1: Process of the developed methodology. Environmental conditions, such as light conditions, lane markings, road edges, and traffic signs, are also considered. Not all parameters are applicable for each run, but they help compare scenarios focusing on similar functions. Different regulations, protocols, and standards often use various terminologies to describe scenarios and define ranges, tolerances, and requirements. Therefore, we unified the terminology in the database to facilitate more effective processing in later steps (especially in the method introduced in Section 2.4). For example, in the case of the movement of the tested vehicle, i.e., Vehicle Under Test (VUT), various behaviors can be mentioned. We categorized these movements according to driving direction, such as turning left ('Farside turn') or right ('Nearside turn'), driving 'Forward' or 'Rearward', or staying 'Stationary'. Similarly, we limited the movement of targets compared to the movement of the VUT to parallel ('Moving parallel'), perpendicular ('Crossing'), and static ('Stationary') directions. In terms of the speed of the VUT and the targets, we also limited it to exact values if ranges or boundaries were given. To ensure no useful information for testing is lost, we created relevant columns for comments where additional information can be provided if the available options do not satisfactorily describe the parameter. The parameters used for building the database, along with explanations where necessary, can be seen in Table 1 of Appendix A. Some of these parameters are selected for further analysis to help compare different runs from a similarity point of view. The method is described in Section 2.4.