AURORA：一种用于理解、推理和编排可靠空地联合仿真的自然语言驱动智能体框架

中文摘要 AI 辅助

空地交通研究日益依赖于联合仿真，然而构建仿真场景仍然劳动密集且难以验证。更重要的是，生成的场景可能成功执行，却未能实现用户要求的空间、时间、通信或行为关系。本文提出了AURORA，一种自然语言驱动的智能体框架，将空地场景生成视为一个带验证的编译过程。AURORA的核心是空地场景图（AGSG），一种类型化的中间表示，显式连接智能体、空中任务、事件、通信链路、成功条件及其跨域依赖。这种共享表示使得在统一工作流中实现基于仿真器的解析、联合道路-空域接地、时间规划、执行前可行性检查、基于轨迹的运行时验证、故障定位和有界修复成为可能。我们进一步引入了AURORA-Bench，以评估生成的场景是否不仅执行成功，而且忠实地实现了请求的交互。跨多个语言模型的实验表明，结构化执行显著提高了可靠性，而运行时验证揭示了基于完成度的评估所忽视的静默故障。局部修复进一步解决了许多违规问题，而无需重新生成整个场景。结果表明，可靠的场景生成需要验证实际行为，而不仅仅是可执行代码，并证明了显式中间表示对于可验证和可修复的语言驱动联合仿真的价值。

英文摘要

Air-ground transportation research increasingly relies on co-simulation, yet constructing scenarios remains labor-intensive and difficult to validate. More importantly, a generated scenario may execute successfully while failing to realize the spatial, temporal, communication, or behavioral relationships requested by the user. This paper presents AURORA, a natural-language-driven agentic framework that treats air-ground scenario generation as a process of compilation with verification. Central to AURORA is the Air-Ground Scenario Graph (AGSG), a typed intermediate representation that explicitly connects agents, aerial missions, events, communication links, success conditions, and their cross-domain dependencies. This shared representation enables simulator-grounded parsing, joint road-airspace grounding, temporal planning, pre-execution feasibility checking, trace-based runtime verification, failure localization, and bounded repair within a unified workflow. We further introduce AURORA-Bench to evaluate not only whether generated scenarios execute, but whether they faithfully realize the requested interactions. Experiments across multiple language models show that structured execution substantially improves reliability, while runtime verification exposes silent failures that completion-based evaluation overlooks. Localized repair further resolves many violations without regenerating the entire scenario. The results show that reliable scenario generation requires verifying realized behavior, not merely executable code, and demonstrate the value of explicit intermediate representations for verifiable and repairable language-driven co-simulation.

发表机构

• Texas A&M University（德克萨斯A&M大学）
• University of Wisconsin–Madison（威斯康星大学麦迪逊分校）

机构由 AI 辅助整理，请以论文原文为准。