A Survey on LLM-Enabled UAV Digital Twins for Internet of Vehicles Systems

Vehicular Digital Twins (VDTs) are emerging as a core enabler for real-time monitoring, simulation, and intelligent control in Internet of Vehicles (IoV) systems. While prior research has largely focused on ground vehicles, the integration of Unmanned Aerial Vehicles (UAVs) into vehicular digital twin ecosystems remains underexplored. This paper presents a comprehensive survey on the role of Large Language Models (LLMs) in enabling UAV Digital Twins for autonomous and cooperative operations within IoV environments. By conceptualizing UAVs as cyber-physical entities continuously synchronized with their virtual counterparts, we analyze how LLMs act as cognitive engines for high-level reasoning, multimodal perception grounding, natural-language interaction, and adaptive mission planning. The survey introduces an LLM-enabled UAV Digital Twin architecture incorporating edge-cloud collaboration, real-time state mirroring, and safety-aware control. Communication workflows leveraging vehicular networking paradigms such as V2X are discussed to support low-latency synchronization and fleet-level coordination. Key application domains, including disaster response, infrastructure monitoring, and Drone-as-a-Service within IoV systems, are examined. Finally, open challenges related to real-time synchronization, computational constraints, and scalability are highlighted, along with future research directions involving TinyLLMs, federated learning, and edge-enabled digital twin platforms.