## 2 Literature Review
### Research on Vehicle Trajectory Generation  
In recent years, data augmentation techniques have been proposed to address datasets with insufficient data, particularly in the context of machine learning. As a new generation of artificial intelligence technologies advances, exemplified by deep neural networks, an increasing number of scholars are beginning to explore the use of deep learning methods for scenario derivation. This approach has become a promising paradigm for deriving edge scenarios beyond traditional theoretical search methods. As a typical unsupervised data augmentation technique, the GAN has a very strong ability to generate realistic data and fake the truth, because it contains the properties of both generative and discriminate networks [29]. This has led to its widespread use in areas such as image enhancement, video generation, text generation, and other fields. The development of GAN models has indeed opened promising avenues for generating hazardous scenarios based on deep learning, addressing the lack of dangerous scenarios in naturalistic driving databases. Vehicle trajectory data, which include precise information on the vehicle's position, speed, and acceleration at each moment, provide rich details for describing dynamic traffic scenarios. The construction of test scenarios in simulation testing requires a critical trajectory to provide information and reflect whether there is adversarial behavior in traffic environments. Therefore, the generation of hazardous scenarios can be considered by using advanced machine learning algorithms to train and learn thegeneration of dangerous traffic vehicle trajectories directly from existing databases [30]. Alternatively, the parameter space of the scenario can be expanded by generating samples that do not exist in the scenario library through a data-driven approach [31]. However, the complex relationships between the attributes and temporal characteristics of consecutive points in time series, such as trajectories, pose an additional challenge for generative models. As a relatively new field for GAN research, there is a significant amount of work being done to develop models capable of generating high-quality, diverse, and specific time series data. Particularly in the finance and medical industries, models such as Noise Reduction GAN [32] and Quant GAN [33] have been proposed, which can capture the dependencies in continuous time series data. The superiority of these methods has been confirmed in studies involving time series data with fewer features, such as stocks. However, their effectiveness in generating trajectory data still requires further research. In the field of trajectory research, many researchers use GAN models to predict motion trajectories [34; 35]. [PERSON] and [PERSON] [36] focused on the definition and generation of latent codes, aiming to solve the prediction of driving trajectories in scenario-based traffic environments. Some researchers have also begun to explore trajectory generation. [PERSON] et al. [30; 31] have designed vehicle trajectory generation models based on GAN and VAE, which can generate new lane-changing trajectories without the need for labeled data. [PERSON] et al. [37] have proposed a deep learning framework driven by real driving data to generate driving trajectories of varying lengths. This framework employs recurrent conditional generative adversarial networks (RC-GANs), which can flexibly generate driving trajectories of variable lengths [38]. It can be found that the generation of vehicle trajectories based on natural driving data with the purpose of generating more scenarios and including temporal features receives less attention. While some researchers have begun to explore the generation of vehicle trajectories using deep learning, their focus has largely been on scenarios such as highways. These scenarios are characterized by straight roads and fewer features in vehicle trajectories.