DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models

Microscopic traffic simulation has become an important tool for autonomous driving training and testing. Although recent data-driven approaches advance realistic behavior generation, their learning still relies primarily on a single real-world dataset, which limits their diversity and thereby hinders downstream algorithm optimization. In this paper, we propose DriveGen, a novel traffic simulation framework with large models for more diverse traffic generation that supports further customized designs. DriveGen consists of two internal stages: the initialization stage uses large language model and retrieval technique to generate map and vehicle assets; the rollout stage outputs trajectories with selected waypoint goals from visual language model and a specific designed diffusion planner. Through this two-staged process, DriveGen fully utilizes large models' high-level cognition and reasoning of driving behavior, obtaining greater diversity beyond datasets while maintaining high realism. To support effective downstream optimization, we additionally develop DriveGen-CS, an automatic corner case generation pipeline that uses failures of the driving algorithm as additional prompt knowledge for large models without the need for retraining or fine-tuning. Experiments show that our generated scenarios and corner cases have a superior performance compared to state-of-the-art baselines. Downstream experiments further verify that the synthesized traffic of DriveGen provides better optimization of the performance of typical driving algorithms, demonstrating the effectiveness of our framework.

@article{zhang2025_2503.05808,
  title={ DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models },
  author={ Shenyu Zhang and Jiaguo Tian and Zhengbang Zhu and Shan Huang and Jucheng Yang and Weinan Zhang },
  journal={arXiv preprint arXiv:2503.05808},
  year={ 2025 }
}