The uniqueness of roundabouts at intersections lies in their connection to surrounding roads through circular paths, with vehicles traveling within the roundabout having higher priority. These arrangements help reduce accident rates and the severity of accidents. However, for multi-lane roundabouts, the traffic conditions are complex, and frequent lane changes by vehicles entering and exiting can create conflict points, leading to increased risk in merging and diverging zones. Moreover, vehicles traveling within the roundabout experience constantly changing speeds and acceleration directions. The use of roundabouts is becoming increasingly common in many countries, and safety issues are becoming more widespread. However, research on key areas of safety at roundabouts, like the merge zones, has not received the attention it deserves, especially in the area of scenario generation. Based on the analysis above, this study proposes a research framework for high-risk scenarios in roundabout environments. The hazardous scenarios are simplified to the trajectories of interacting vehicles. A generative model that combines autoregressive modeling and adversarial network learning captures the characteristics of vehicle interactions, preserves the dynamic information of temporal trajectories, and derives and generalizes more key scenarios for autonomous driving tests. Additionally, the quality of the derived key roundabout scenarios is assessed from three perspectives: diversity, similarity, and authenticity. Finally, a simulation model is established to test the generated scenarios, including a new simulation method that validates the applicability and importance of the generated scenarios for autonomous driving tests. The following are this study's primary contributions:  
1. To preserve the characteristics of vehicle operation within roundabouts, a modified two-dimensional time-to-collision (MTTC) model is used to identify high-risk scenarios in merging areas and to pinpoint interacting vehicles that may pose collision risks. To enhance the efficiency of derivation and simplify scenario information, the focus of the study is shifted to driving trajectories, making the method more generalizable and applicable. 2. A time series generative network is introduced to generate key scenarios at roundabouts, effectively capturing the motion characteristics of interacting vehicle pairs and the temporal features of sequential trajectories. The evaluation metrics for the produced outcomes are improved, verifying the diversity of the derived scenarios, their similarity to real-world situations, and the probability that they would materialize in the real world from different perspectives. 3. A simulation experiment scenario is established, and a new test method is developed that specifies the trajectory of one vehicle and the starting point of another to assess the risk of interaction between the vehicles. The following is the structure of this paper: Section 2 offers an overview of the literature. Section 3 describes the research methods, including data processing, model generation, evaluation of generated scenarios, CARLA simulation (an open urban driving simulator), and Simulation of Urban Mobility (SUMO). Section 4 presents the results and discussion, and the paper ends with conclusions in Section 5. For the sake of clarity and ease of reference, all acronyms used throughout this manuscript are defined in the following Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Definition** \\ \hline SUMO & Simulation of Urban Mobility \\ CARLA & An open urban driving simulator \\ SSM & Surrogate safety measures \\ TTC & Time-to-collision \\ MTTC & Modified two-dimensional time-to-collision \\ MSV & The merging subject vehicle \\ NRV & The nearby roundabout vehicle \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The definitions of the acronyms.