## 2 Materials and Methods
### Defining Comparison Parameters: Aligning Questionnaire Responses with Database  
To select the appropriate critical test cases, the vehicle manufacturer or designers must first fill out the questionnaire found in Appendix B. As mentioned in Section 2.1, a database of more than 1000 test runs was developed, with various parameters defined for each scenario. Our goal is to select the relevant scenarios from this database based on the answers from the questionnaire. To ensure that the parameters used in test scenario database can be matched with the responses given in the questionnaire, we need to ensure compatibility. The process is illustrated in Figure 2. As previously mentioned, some questions are based on the analysis of database parameters and past experiences, allowing for certain test cases to be directly linked to relevant parameters. Out of the 23 vehicle-related questions in the questionnaire, 13 are used for selecting test cases. The remaining questions are more relevant for further vehicle assessment and functional safety analysis, which are not the focus of this work but are pertinent to the activities at a TIC company. Among the 13 questions, some are 'yes/no' questions, while others allow for multiple answers. The responses are processed automatically using appropriate Microsoft Excel(r) functions. For questions with multiple answer options, responses are stored in several columns using the one-hot encoding method. However, for some questions with multiple options, we only examine the presence of a capability without using the specific content of the answer for test case selection. Based on the responses, a unit vector consisting of 22 dimensions is created. Each of the 22 columns contains a relevant parameter, as shown in Table A2 in Appendix A. To select the appropriate test scenarios, comparable vectors must be created from each row of the database. Initially, we manually pre-processed the database, extending it with additional parameters that follow the guidelines used when creating the questionnaire, including the following:  
* Purpose (category of M or N);
* Dimension (1--\(\leq\)3.5 t; 2--\(\leq\)5 t; 3--\(\leq\)12 t; 4--\(\geq\)12 t);
* Target ODD (motorway (MW), rural road (RR), urban area (UA));
* Speed groups (1--v \(\leq\) 30 km/h; 2--30 \(<\) v \(\leq\) 50 km/h; 3--50 \(<\) v \(\leq\) 100 km/h; 4--v > 100 km/h);
* Lane keeping;
* Stopping in front of static objects;
* Stopping in front of moving objects;
* Suddenly appearing objects;
* Minimum following distance;
* Detection range;
* Lane changing;
* Turning;
* Handling of traffic signs and rules;
* Handling of junctions;
* Relevance for standing passengers;
* Presence of driver;
* Parking;
* Reversing. During pre-processing, we mainly examined whether the individual test cases were relevant to the given attribute and, in some cases, filled out the column based on values found in the original database (e.g., categorizing test runs into 1-4 categories based on speed, weight). In some cases, we also analyzed the test runs based on the applicability of  
Figure 2: Process of evaluating questionnaire responses and test run database to generate the similarity vector. a given parameter such as the typical ODD. Following this step, we used Microsoft Excel(r) functions to automatically create a 22-element unit vector from each row, similar to the questionnaire. The explanation for vector creation related to both the extended database and the questionnaire is provided in Table 12 of Appendix A.  
Vectorizing data is a common approach when comparing large datasets. It is especially used in machine learning for tasks such as comparing different texts, images, and identifying relevant choices based on user preferences. To measure the similarity between vectors or datasets, various methods can be used. The most commonly used methods are Euclidean and Manhattan distances and cosine similarity [29; 30; 31].