用大模型提升交通模拟可控性，生成更复杂多样的真实场景。
Controllable Traffic Simulation through LLM-Guided Hierarchical Reasoning and Refinement
- 分层推理+自反思机制，让大模型逐步理解交通描述
- 在Waymo数据集上生成更多样、更复杂的可控交通场景
- 基于弗雷内坐标的代价函数，提升空间关系理解能力
通过可控仿真评估自动驾驶系统在复杂多变交通场景中的表现，对确保其安全性和可靠性至关重要。然而现有交通仿真方法在可控性方面存在挑战。为此，我们提出一种基于扩散模型并增强大语言模型的新型交通仿真框架。该方法包含高层理解模块与低层精修模块，系统分析交通元素的层次结构，引导大模型逐步深入解析交通场景描述，并通过自我反思实现生成优化，从而提升对复杂情境的理解能力。此外，我们设计了一种基于弗雷内坐标系的代价函数框架，为大模型提供具有几何意义的量度，改善其对场景中空间关系的把握，实现更精准的代价函数生成。在Waymo开放运动数据集（WOMD）上的实验表明，本方法能够处理更复杂的描述，以可控方式生成更广泛多样的交通场景。
原文摘要 · Abstract (English)
Evaluating autonomous driving systems in complex and diverse traffic scenarios through controllable simulation is essential to ensure their safety and reliability. However, existing traffic simulation methods face challenges in their controllability. To address this, we propose a novel diffusion-based and LLM-enhanced traffic simulation framework. Our approach incorporates a high-level understanding module and a low-level refinement module, which systematically examines the hierarchical structure of traffic elements, guides LLMs to thoroughly analyze traffic scenario descriptions step by step, and refines the generation by self-reflection, enhancing their understanding of complex situations. Furthermore, we propose a Frenet-frame-based cost function framework that provides LLMs with geometrically meaningful quantities, improving their grasp of spatial relationships in a scenario and enabling more accurate cost function generation. Experiments on the Waymo Open Motion Dataset (WOMD) demonstrate that our method can handle more intricate descriptions and generate a broader range of scenarios in a controllable manner.
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