## 1 Introduction  
According to a report by the World Health Organization on road safety, the number of road fatalities worldwide exceeded 1.35 million in 2016 [1]. Automated driving systems (ADSs) with the Society of Automotive Engineers (SAE) automation levels [2] of level 3 or higher, also called highly automated driving systems (HADSs), are expected to reduce these fatalities. Furthermore, vehicles equipped with such HADSs are expected to reduce traffic congestion and fuel consumption and provide better transportation. Many companies and research institutes are conducting research and development on these systems. In HADSs, the responsibility lies with the system and not the driver. Thus, a thorough safety assessment of the system is required, particularly when introducing the system to the market. Conventionally, mileage-based assessments have been performed to evaluate the safety of these systems. This assessment calculates the average driving distance between two accidents by ensuring a sufficient driving distance for system tests [3]. However, the driving distance required to test the safety of HADSs is predicted to be billions of kilometers or more [4; 5], which is not feasible. The driving distance for mileage-based assessments of ADSs becomes unfeasibly long because they need to encounter various critical scenarios in public traffic, including rare ones, and assess the system in those scenarios. In contrast to this mileage-based assessment, scenario-based assessment has been employed in various studies [6; 7; 8; 9]. Scenario-based assessment is a method in which critical scenarios for ADS are intentionally generated and the response of the systems is evaluated in virtual simulations and real-world tests [10; 11]. If critical scenarios are identified in advance, this method makes it possible to evaluate the performance of a system in randomly generated critical scenarios that cannot be considered in mileage-based assessments without driving long distances. Therefore, this method can significantly reduce the driving distance and effort required to test an ADS compared to conventional mileage-based assessments. Identifying sufficient critical scenarios inside the operational design domain of ADS is a significant challenge for conducting a scenario-based assessment efficiently. [PERSON] et al. [12] classified scenarios into three levels, functional, logical, and concrete, according to the granularity of information. Functional scenarios are abstract scenarios described in a language format that humans can easily understand. The scenarios contain information on the sequence and behavior of the scenario components. Logical scenarios have parameter degrees of freedom described in a machine-processable form. Information on models and parameters representing elements and behaviors is assigned, and parameters are provided in ranges, distributions, or constraints between the parameters. Concrete scenarios are those with specific parameters, which satisfy the range and distribution in logical scenarios. These scenarios are used for safety assessment. A five-layer model provides a structured description of the environmental factors in a scenario [13]. The complex urban environment is deemed challenging for ADSs [14], and their critical scenarios need to be identified. In urban environments, there are differences in highways in terms of road geometry (e.g., intersections), traffic regulation (e.g., traffic lights), and traffic participants (e.g., pedestrians and cyclists). Road geometry, traffic regulations, and traffic participants correspond to Layers 1, 2, and 4 of the five-layer model, respectively. These urban environmental features must be considered in test scenarios. Thus, it is essential to understand the environmental factors (scenario components) that lead to safety-critical events (e.g., accidents and near-miss incidents) to identify the critical scenarios.