## 4 Results and Discussion
### Evaluation of Generated Data
#### 4.1.2 Similarity and Authenticity  
1. Kinematic Feature Analysis  
**Velocity distributions:** Figure 13 depicts the speed distributions of interacting vehicle pairs, both in the horizontal and vertical directions. Figure 12(a) exhibits the velocity distri  
Figure 11: Visualization results after dimensionality reduction. Figure 12: Scenario visualization. butions of MSVs in the original and generated scenarios for all samples. Figure 12(b) exhibits the velocity distributions of NRVs. The TimeGAN model used in this study used coordinate data as the output and input results, so the original and generated data were highly consistent in the distribution of coordinate values. It would be more rigorous to look at the strengths and weaknesses of the generated data even further by calculating the velocities. For the obtained velocity results in the distribution, in the generated scenario both in the horizontal and vertical directions, the distribution pattern was very close to the real ones. For the MSV, as it is a merging vehicle, it tended to slow down and observe before entering the roundabout, resulting in speeds predominantly concentrated between [\(-\)5, 5] m/s. On the other hand, the NRV maintained a steady speed while navigating the roundabout. Although it did not decelerate to yield, its speed during roundabout traversal remained relatively slow, with speeds concentrated in the range [2, 8] m/s regardless of the vehicle's position. These similarities in the distribution of speeds indicate that the TimeGAN model effectively captured the motion characteristics of both the MSV and NRV. Although there may be slight differences in mathematical features, such as maximum/minimum values, as shown in Table 2, these differences were within expectations and consistent with real-world scenarios. **Distance distributions:** Figure 14 illustrates the distances between interacting vehicle pairs in the horizontal and vertical directions. The overall distribution patterns of generated and real-world scenario data were consistent. The distances in both directions were mainly concentrated in the range of [\(-\)20, 20] m. This consistency was due to the structural  
Figure 13: Comparison of speed distribution between real and generated scenarios. where the MTTC was less than 3 s, whereas in the real scenarios, there were more points where the MTTC was greater than 3 s. In some extreme cases, the generated scenarios exhibited bolder characteristics. For instance, while real-world scenarios did not feature MTTC values less than 1 s, such instances were observed in generated scenarios. Overall, the generated trajectory data exhibited a high level of similarity with real trajectory data in terms of distribution and extremum values. However, the generated trajectory data often displayed more hazardous features, such as higher speeds and shorter distances, leading to riskier merging scenarios. This demonstrates that TimeGAN can effectively capture the characteristics of real-world scenarios and is suitable for generating adversarial cases with high risk. It can even generate more dangerous and more valuable adversarial scenarios, which provides a new way to generate dangerous scenarios and is an effective way to expand the library of test scenarios in favor of CAV testing. 3. Model Evaluation for Similarity and Authenticity  
To evaluate the similarity and authenticity of the generated data, two separate models based on recurrent neural networks (RNNs) were developed: one for time series classification and the other for forecasting. The time series classification model discriminated between generated and real data to assess the classification accuracy. The closer the discriminant score of the classification model was to 0, the higher the performance of the classification model, which means that the data generated by the TimeGAN model were of higher quality. A smaller MAE value indicated better prediction performance, which further indicated a higher quality of the data generated by the TimeGAN model.