Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to provide flexibility and scalability. We propose LASER, a novel frame-work that leverage large language models (LLMs) to conduct traffic simulations based on natural language inputs. The framework operates in two stages: it first generates scripts from user-provided descriptions and then executes them using autonomous agents in real time. Validated in the CARLA simulator, LASER successfully generates complex, on-demand driving scenarios, significantly improving ADS training and testing data generation.
@article{arxiv.2410.16197,
title = {LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation},
author = {Hao Gao and Jingyue Wang and Wenyang Fang and Jingwei Xu and Yunpeng Huang and Taolue Chen and Xiaoxing Ma},
journal= {arXiv preprint arXiv:2410.16197},
year = {2024}
}