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AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation
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 explicitl
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-17T00:41:46.000Z
- arXiv · Artificial Intelligence · 2026-09-17T00:41:46.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.