Advancing Autonomous Emergency Response Systems: A Generative AI Perspective

Abstract--Autonomous V ehicles (A Vs) are poised to revolutionize emergency services by enabling faster, safer, and more efficient responses. This transformation is driven by advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL), which allows A Vs to navigate complex environments and make critical decisions in real time. However, conventional RL paradigms often suffer from poor sample efficiency and lack adaptability in dynamic emergency scenarios. This paper reviews next-generation A V optimization strategies to address these limitations. We analyze the shift from conventional RL to Diffusion Model (DM)-augmented RL, which enhances policy robustness through synthetic data generation, albeit with increased computational cost. Additionally, we explore the emerging paradigm of Large Language Model (LLM)-assisted In-Context Learning (ICL), which offers a lightweight and interpretable alternative by enabling rapid, on-the-fly adaptation without retraining. By reviewing the state of the art in A V intelligence, DM-augmented RL, and LLM-assisted ICL, this paper provides a critical framework for understanding the next generation of autonomous emergency response systems from a Generative AI perspective. Autonomous vehicles (A Vs) are poised to transform emergency services by enabling faster, safer, and more intelligent responses. Uncrewed Aerial V ehicles (UA Vs), as key enablers within the A V ecosystem, provide rapid deployment and precise mobility. They can serve as both aerial base stations and data collectors, enhancing connectivity and information gathering for A V operations.