LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation
No official code found. Best implementation: NJUDeepEngine/LASER (2 stars, MIT, last commit 2025-06-20). 1 more below.
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 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.
