driving scenarios as possible before putting them into use, and discover existing safety problems are the current challenges. One of the current major test methods is the road test, but it is time-consuming, inefficient, and costly. Similar low-risk scenarios make up the majority of real road test scenarios, whereas less frequently occur more hazardous circumstances with significant testing value. To guarantee sufficient safety of autonomous vehicles through real-world road testing, it will be expensive and take a long time. Researchers have suggested that approximately 99% of testing and verification work should be conducted in virtual environments [3], with only about 0.9% taking place on actual test sites. As a result, scenario-based virtual simulation testing can lessen the workload associated with field testing while better satisfying the needs for extensive and effective autonomous driving testing. At present, testing scenarios can be classified into typical scenarios and edge scenarios according to the requirements. Typical scenarios are generally used for routine functional testing of autonomous driving systems, typically selected from real traffic data as representative test cases, but they cannot cover all real-world conditions. For testing autonomous vehicles, it is crucial not only to focus on the safety of various vehicle functions in normal driving scenarios, but also to emphasize the vehicle's ability to safely navigate challenging testing scenarios. Selecting high-risk scenarios from numerous complex traffic situations has become a focal point [4; 5]. Thus, edge scenarios complement typical scenarios and are described as low-probability event scenarios that are difficult to handle, lying between the boundaries of danger and safety. At the same time, autonomous driving scenarios exhibit high levels of uncertainty, with a complexity that is difficult to exhaustively cover. To enhance scenario coverage, it is necessary to evolve new derivative scenarios from real traffic data. Currently, methods for generating such critical scenarios include optimization search-based generation [6; 7; 8], reinforcement learning-based generation [9; 10; 11], importance sampling-based generation [12; 13; 14], and deep-learning-based generation [15; 16; 17]. However, whether the characteristics of derived scenarios are consistent with real traffic data remains unknown. It is possible to generate scenarios that do not exist in the real world, so derived scenarios need to be validated for authenticity before use. As a primary component of scenarios, the driving states reflected by vehicle trajectories can be utilized to simulate the dynamic changes in driving behaviors, thereby supporting the construction of diverse and realistic testing scenarios. However, since challenging scenarios are very rare in the actual traffic field, resulting in difficulties in obtaining sufficient safety-critical trajectories by means of data collection, most of the naturalistic driving data collected are safe [18]. Considering that trajectories involving high-risk confrontational behavior between surrounding vehicles (called safety-critical trajectories) are integral to simulation testing, a generative approach to obtaining vehicle trajectories is used to address this issue [19]. Deep learning methods can model key trajectory data from naturalistic driving trajectories, capturing driving patterns within the dataset and generating uncovered, realistic, and safety-critical driving trajectories. These novel scenarios evolved from existing time series traffic trajectory data and greatly enrich the diversity of the test scenario library samples, improving test coverage. The selected research object in this study, the roundabout, is a type of complex and critical scenario. Identifying challenging edge scenarios within it and deriving them should be a focus of attention for autonomous driving.