The effectiveness of collision-free trajectory planners depends on the quality and diversity of training data, especially for rare scenarios. A widely used approach to improve dataset diversity involves generating realistic synthetic traffic scenarios. However, producing such scenarios remains difficult due to the precision required when scripting them manually or generating them in a single pass. Natural language offers a flexible way to describe scenarios, but existing text-to-simulation pipelines often rely on static snippet retrieval, limited grammar, single-pass decoding, or lack robust executability checks. Moreover, they depend heavily on constrained LLM prompting with minimal post-processing. To address these limitations, we introduce ARISE - Adaptive Refinement and Iterative Scenario Engineering, a multi-stage tool that converts natural language prompts into executable Scenic scripts through iterative LLM-guided refinement. After each generation, ARISE tests script executability in simulation software, feeding structured diagnostics back to the LLM until both syntactic and functional requirements are met. This process significantly reduces the need for manual intervention. Through extensive evaluation, ARISE outperforms the baseline in generating semantically accurate and executable traffic scenarios with greater reliability and robustness.

翻译：无碰撞轨迹规划器的有效性取决于训练数据的质量与多样性，尤其对于罕见场景而言。提升数据集多样性的一种广泛采用的方法是生成逼真的合成交通场景。然而，由于手动编写脚本或单次生成所需的高精度要求，此类场景的生成仍然困难。自然语言为描述场景提供了一种灵活的方式，但现有的文本到仿真流程通常依赖于静态片段检索、受限语法、单次解码，或缺乏鲁棒的可执行性检查。此外，它们严重依赖于约束性的大语言模型提示，且后处理极少。为应对这些局限，我们提出了ARISE——自适应优化与迭代场景工程，这是一种多阶段工具，通过迭代的大语言模型引导优化，将自然语言提示转换为可执行的Scenic脚本。在每次生成后，ARISE会在仿真软件中测试脚本的可执行性，并将结构化诊断信息反馈给大语言模型，直至满足语法和功能要求。这一过程显著减少了人工干预的需求。通过广泛评估，ARISE在生成语义准确且可执行的交通场景方面优于基线方法，展现出更高的可靠性与鲁棒性。