用自然语言生成自动驾驶测试场景，实时执行。
LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation
- 用大模型将自然语言转为交通场景脚本
- 在CARLA中实时运行，生成复杂测试场景
- 适合自动驾驶研发团队快速构建测试数据
自动驾驶系统（ADS）需要多样且安全关键的交通场景用于有效训练与测试，但现有数据生成方法难以兼顾灵活性与可扩展性。本文提出LASER，一种基于大语言模型（LLMs）的框架，可根据自然语言输入生成交通模拟场景。该框架分两阶段运行：首先从用户描述生成场景脚本，随后在实时环境中通过自主代理执行。在CARLA仿真器中验证表明，LASER成功生成复杂、按需定制的驾驶场景，显著提升了自动驾驶系统训练与测试数据的生成效率。
原文摘要 · Abstract (English)
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.
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