## 5 Conclusions  
This study developed a data-driven deep learning framework for generating adversarial hazardous scenarios and devised metrics to evaluate the quality of the generated data. Joint simulation was used to highlight the significance of the generated scenarios. The original data were sourced from the RounD dataset, focusing on vehicle merging scenarios at a genuine roundabout, and extracting hazardous scenarios with adversarial behaviors. The original complex scenario data were culled to leave the trajectory data with critical content, and the dimensions were reduced to horizontal and vertical coordinates at each timeframe. The dynamic information in time series was captured using TimeGAN, which combines adversarial network learning with autoregressive models. The quality of generated lane-changing scenario data was assessed from three critical perspectives: diversity, similarity, and authenticity. Finally, the generated scenarios were tested through the joint simulation using CARLA and SUMO, which confirmed the necessity of generated scenarios in autonomous driving testing. The main conclusions are as follows:  
1. Simplifying scenarios into trajectory data for generation is a rational and effective approach. It allows for the retention of maximum information while enhancing the performance of model training.
2. For complex scenarios like roundabouts, TimeGAN effectively captured the spatiotemporal features of merging scenarios. It demonstrated the diversity of the generated data, the similarity between the generated and real data, and the feasibility of the generation in various aspects. The analysis validated the effectiveness of this method in expanding the test scenario library.
3. The developed TimeGAN model effectively generated more high-risk merging scenarios, including adversarial situations not observed in real scenarios. This demonstrates the great potential of generative methods in hazardous scenario testing.  
This study still has certain shortcomings. First off, while the TimeGAN model has been investigated for equal-length data, it has not yet been tested for asymmetrical time step sequences. Moreover, it is also necessary to investigate whether the critical scenarios that are important for CAV testing can be further explored in the generated model. The next step to investigate is whether working together with a driving simulator to calibrate  
Figure 16: Simulation results.  
cars for different forms of autonomous driving and implement the CAV's control module functionalities to be tested in the simulation program is feasible.  
Conceptualization, D.R. and Y.L.; methodology, D.R. and J.J.; validation, D.R.; resources, Y.L.; data curation, J.J.; writing--original draft preparation, D.R.; writing--review and editing, Y.L. and J.J.; project administration, H.H. and Y.L.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2023 YFB2504704, Changsha Major Science and Technology Projects, grant number kh2401002, the Ministry of Education Foundation for Humanities and Social Sciences, grant number 24 YJCZH460, and the Natural Science Foundation of Hunan Province, China, grant number 2024 JJ7624.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.