统一空中与地面智能体仿真，支持高保真城市环境与多模态感知同步。
CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence
- 基于Unreal Engine整合驾驶与飞行物理引擎，实现空地一体仿真。
- 每帧同步捕获18种传感器数据，支持真实交通与无人机动力学。
- 兼容原有接口，适合研究空地协作、导航与强化学习等任务。
低空经济、具身智能与空地协同系统的发展，推动对能统一建模空中与地面智能体的仿真基础设施的需求。现有开源平台仍存在领域割裂：驾驶模拟器缺乏空中动力学，多旋翼模拟器缺少真实地面场景。基于桥接的联合仿真引入同步开销，且无法保证严格时空一致性。我们提出CARLA-Air，一个开源基础设施，在单一Unreal Engine进程中统一高保真城市驾驶与物理准确的多旋翼飞行。该平台保留CARLA和AirSim原生Python API及ROS 2接口，支持零修改代码复用。在共享物理步与渲染管线中，CARLA-Air提供照片级真实感环境，包含规则合规交通、社交意识行人与气动一致的无人机动力学，并在每帧同步捕获所有平台上的18种传感器模态数据。平台支持空地具身智能典型任务，涵盖协作、具身导航与视觉语言动作、多模态感知与数据集构建、基于强化学习的策略训练。可扩展资产管道支持自定义机器人平台集成。通过继承AirSim的飞行能力（其上游开发已归档），CARLA-Air确保这一广泛采用的飞行栈能在现代基础设施中持续演进。项目提供预编译二进制与完整源码：https://github.com/louiszengCN/CarlaAir
原文摘要 · Abstract (English)
The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation infrastructure capable of jointly modeling aerial and ground agents within a single physically coherent environment. Existing open-source platforms remain domain-segregated: driving simulators lack aerial dynamics, while multirotor simulators lack realistic ground scenes. Bridge-based co-simulation introduces synchronization overhead and cannot guarantee strict spatial-temporal consistency. We present CARLA-Air, an open-source infrastructure that unifies high-fidelity urban driving and physics-accurate multirotor flight within a single Unreal Engine process. The platform preserves both CARLA and AirSim native Python APIs and ROS 2 interfaces, enabling zero-modification code reuse. Within a shared physics tick and rendering pipeline, CARLA-Air delivers photorealistic environments with rule-compliant traffic, socially-aware pedestrians, and aerodynamically consistent UAV dynamics, synchronously capturing up to 18 sensor modalities across all platforms at each tick. The platform supports representative air-ground embodied intelligence workloads spanning cooperation, embodied navigation and vision-language action, multi-modal perception and dataset construction, and reinforcement-learning-based policy training. An extensible asset pipeline allows integration of custom robot platforms into the shared world. By inheriting AirSim's aerial capabilities -- whose upstream development has been archived -- CARLA-Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure. Released with prebuilt binaries and full source: https://github.com/louiszengCN/CarlaAir
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