Sky-Drive: A Distributed Multi-Agent Simulation Platform for Socially-Aware and Human-AI Collaborative Future Transportation

Recent advances in autonomous system simulation platforms have significantly enhanced the safe and scalable testing of driving policies. However, existing simulators do not yet fully meet the needs of future transportation research, particularly in modeling socially-aware driving agents and enabling effective human-AI collaboration. This paper introduces Sky-Drive, a novel distributed multi-agent simulation platform that addresses these limitations through four key innovations: (a) a distributed architecture for synchronized simulation across multiple terminals; (b) a multi-modal human-in-the-loop framework integrating diverse sensors to collect rich behavioral data; (c) a human-AI collaboration mechanism supporting continuous and adaptive knowledge exchange; and (d) a digital twin (DT) framework for constructing high-fidelity virtual replicas of real-world transportation environments. Sky-Drive supports diverse applications such as autonomous vehicle (AV)-vulnerable road user (VRU) interaction modeling, human-in-the-loop training, socially-aware reinforcement learning, personalized driving policy, and customized scenario generation. Future extensions will incorporate foundation models for context-aware decision support and hardware-in-the-loop (HIL) testing for real-world validation. By bridging scenario generation, data collection, algorithm training, and hardware integration, Sky-Drive has the potential to become a foundational platform for the next generation of socially-aware and human-centered autonomous transportation research. The demo video and code are available at:this https URL

@article{huang2025_2504.18010,
  title={ Sky-Drive: A Distributed Multi-Agent Simulation Platform for Socially-Aware and Human-AI Collaborative Future Transportation },
  author={ Zilin Huang and Zihao Sheng and Zhengyang Wan and Yansong Qu and Yuhao Luo and Boyue Wang and Pei Li and Yen-Jung Chen and Jiancong Chen and Keke Long and Jiayi Meng and Yue Leng and Sikai Chen },
  journal={arXiv preprint arXiv:2504.18010},
  year={ 2025 }
}