Advancing Autonomous Emergency Response Systems: A Generative AI Perspective

TL;DR

The paper addresses the need for robust, data-efficient autonomous emergency response in AVs, where conventional RL struggles with sample inefficiency and dynamic adaptation. It examines two generative-AI paradigms: Diffusion Model-augmented Reinforcement Learning (DM-augmented RL) to generate high-fidelity synthetic data, enable generative planning, and improve world modeling, and LLM-assisted In-Context Learning (ICL) to provide lightweight, training-free adaptation via edge-deployed prompts. DM-augmented RL demonstrates superior multi-agent coordination and offline performance, while LLM-assisted ICL offers rapid, interpretable decision-making without retraining; both approaches present complementary strengths and trade-offs. Collectively, these frameworks offer a roadmap for next-generation autonomous emergency systems, improving safety, responsiveness, and deployment scalability in ITS-enabled public safety operations.

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 allows AVs 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 AV 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 AV 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.

Figures (3)

• Figure 1: Illustration of public safety UAV scenario, where the UAV follows a trajectory and collects sensory data. LLM-assisted ICL and DM-augmented RL can be used to optimize its operations.
• Figure 2: Comparison of generative model performance in coordinating a 4-UAV swarm: DMs, GANs and VAEs.
• Figure 3: Convergence of the AIC-VDS framework and baselines.