I. Introduction
The safety of autonomous driving systems relies heavily on the richness of the dataset scenarios. However, due to various constraints such as safety issues, geographical environment, and weather changes, it is difficult for collected data to cover all situations. This poses challenges for training and evaluating planning and prediction modules, especially for extreme scenarios. To address this, simulators such as SUMO [1] and CARLA [2] have been used to manually set scenarios. While rule-based simulations offer interpretability and viable trajectories without extensive data, they have limitations in accuracy, generalization, and adaptability. Furthermore, they require substantial expert knowledge to establish the necessary rules.