Paper ID: 2410.16197

LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation

Hao Gao, Jingyue Wang, Wenyang Fang, Jingwei Xu, Yunpeng Huang, Taolue Chen, Xiaoxing Ma

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.

Submitted: Oct 21, 2024