## 2 Related Works  
Several studies have proposed methods to identify critical concrete scenarios [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. [PERSON] et al. [15] and [PERSON] et al. [16] proposed similar methods using actual driving data. Both methods identify logical scenarios based on driving data and then identify critical scenarios by sampling the combination of scenario parameters and assessing them with certain criticality metrics. Different criticality metrics have been used in other studies to identify critical scenarios. For example, [PERSON] et al. [17] used the \"Risk Perception\" [27] to evaluate the risk of cut-in maneuvers, and [PERSON] and [PERSON] [18] used a metric that considers where an automated vehicle can drive safely. [PERSON] et al. [19] utilized the maximum inverse time-to-collision to divide the parameter space into safe and hazardous zones. [PERSON] et al. [20] proposed a method based on time-to-react measurement and incorporating the concept of variable criticality thresholds to assess the scenario's overall criticality. [PERSON] et al. [21] proposed a method to generate test cases by applying heuristics to naturalistic driving data. [PERSON] et al. [22] further developed the method in [21] by adding boundary functions and a random sampling approach to improve the criticality and coverage of the generated test cases. [PERSON] et al. [23] proposed a method to identify test scenarios as an extension of the systems theoretic process analysis. In addition to using real driving data, some studies utilized simulations [24; 25] and traffic accident data [26] to identify concrete scenarios that are critical for ADSs. In contrast to the aforementioned studies, which directly identify test cases for ADSs, there are studies that focus on identifying critical functional/logical scenarios [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. [PERSON] et al. [28] identified hazardous scenarios based on environmental conditions that may trigger a hazard, considering the system specifications of ADS. Similarly to [28], [PERSON] et al. [29] considered laws and regulations, in addition to system specifications. [PERSON] et al. [30] proposed a framework to define safety-related scenarios based on a potential collision between an ego vehicle and an object that forces it to change its planned action. [PERSON] et al. [31] proposed a method to determine sufficient parameters to describe the scenarios and a metric to assess the representativeness of a scenario in real-world scenarios derived from driving data. [PERSON] et al. [32] identified pre-crash scenarios at road junctions using car-crash data and a clustering method. [PERSON] et al. [33] used the accident database IGLAD, which contains accident data from different countries, to identify and compare the critical scenarios in different countries. [PERSON] et al. [34] customized a vision transformer that classifies critical situations around traffic accidents using an accident dataset and proposed a framework to derive scenarios automatically. Other studies identified scenarios using police reports of traffic accidents [35; 36], accident data included in a naturalistic driving study (NDS) [37], and near-miss incidents in a microscopic traffic simulation [38]. In most of the methods that identify concrete scenarios, the factors considered in the scenario are provided, and the methods search for a critical scenario inside a fixed parameter space. Regarding the methods for identifying functional/logical scenarios based on a specific data source (e.g., traffic accident data), the description of the identified scenario is limited to the content present in the data source. When identifying critical scenarios in urban environments, it is essential to consider environmental factors that may trigger safety-critical events in complex environments. However, it is difficult to consider scenarios with these factors in most existing methods because the scenario space is a given parameter, or the data source used in the methods limits the scenario space.