CARLA-Air：在CARLA世界中操控無人機——面向空陸具身智能的統一基礎設施
CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence
March 30, 2026
作者: Tianle Zeng, Hanxuan Chen, Yanci Wen, Hong Zhang
cs.AI
摘要
低空經濟、具身智能與空地協同系統的融合發展，正催生對能夠在統一物理環境中聯合建模空中與地面智能體的仿真基礎設施的迫切需求。現有開源平台存在領域割裂問題：駕駛仿真器缺乏空中動力學模型，而多旋翼仿真器則缺少真實地面場景。基於橋接的聯合仿真方案會引入同步開銷，且無法保證嚴格的時空一致性。
我們推出CARLA-Air這一開源基礎設施，通過單個Unreal Engine進程實現高保真城市駕駛與精確物理建模的多旋翼飛行仿真統一。該平台完整保留CARLA與AirSim原生的Python API及ROS 2接口，支持零修改代碼復用。在共享的物理時步與渲染管線中，CARLA-Air提供具備規則合規交通流、社會化感知行人模型與空氣動力學一致無人機動態的逼真環境，並在每個時步同步採集所有平台共計18種傳感器模態數據。平台支持具身導航與視覺語言行動、多模態感知與數據集構建、基於強化學習的策略訓練等典型空地具身智能任務，並通過可擴展資源管線實現自定義機器人平台與共享世界的無縫集成。
通過繼承AirSim（其上游開發已歸檔）的航空仿真能力，CARLA-Air確保這一廣受採用的飛行仿真技術棧能在現代化基礎設施中持續演進。平台現已發佈預編譯二進制文件與完整源代碼：https://github.com/louiszengCN/CarlaAir
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 By inheriting AirSim's aerial capabilities -- whose upstream development has been archived -- CARLA-Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure.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