Advancing Autonomous Emergency Response Systems: A Generative AI Perspective

Abstract

Autonomous Vehicles (AVs) 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 enables AVs to navigate complex environments and make critical decisions in real time. However, conventional RL paradigms often exhibit limited sample efficiency and insufficient adaptability in highly dynamic emergency scenarios. This paper reviews next-generation AV optimization strategies that address these limitations. We analyze the shift from conventional RL to Diffusion Model (DM)-augmented RL, which improves policy robustness through synthetic data generation, at the cost of increased computational complexity. Additionally, we explore the emerging paradigm of Large Language Model (LLM)-assisted In-Context Learning (ICL), which offers a lightweight, interpretable alternative by enabling rapid, on-the-fly adaptation without retraining. By reviewing the state of the art in AV intelligence, DM-augmented RL, and LLM-assisted ICL, this paper offers a critical framework for understanding the next generation of autonomous emergency response systems from a Generative AI (GenAI) perspective.