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

Hao Gao, Yunpeng Huang, Jingwei Xu, Taolue Chen, Xiaoxing Ma

Keywords: Traffic Simulation; Autonomous Agents; LLM for Autonomous Driving

Abstract: 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 framework 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.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 3543