Sky-Drive: a Distributed Multiagent Simulation Platform for Human-AI Collaborative and Socially Aware Future Transportation

Recent advances in autonomous system simulation platforms have significantly enhanced the safe and scalable testing of driving policies. Although existing simulators have greatly accelerated development by providing controlled testing environments, they face limitations in addressing the evolving needs of future transportation research, particularly in enabling effective human-artificial intelligence (human-AI) collaboration and modeling socially aware driving agents. This study introduces Sky-Drive, a novel distributed multiagent simulation platform that addresses these limitations through four key innovations: (1) a distributed architecture for synchronized simulation across multiple terminals; (2) a multimodal human-in-the-loop framework that integrates diverse sensors to collect rich behavioral data; (3) a human-AI collaboration mechanism that supports continuous and adaptive knowledge exchange; and (4) a digital twin framework for constructing high-fidelity virtual replicas of real-world transportation environments. Sky-Drive supports diverse applications, such as autonomous vehicle-human road user interaction modeling, human-in-the-loop training, socially aware reinforcement learning, personalized driving development, and customized scenario generation. Future extensions will incorporate foundation models for context-aware decision support and hardware-in-the-loop 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 human-centered and socially aware autonomous transportation system research.