推进自主应急响应系统：生成式AI的视角
Advancing Autonomous Emergency Response Systems: A Generative AI Perspective
AI总结：
本文从生成式AI视角探讨自动驾驶在应急响应中的应用，分析了扩散模型增强的强化学习和大型语言模型辅助的上下文学习，以提升系统鲁棒性和适应性。
摘要：
自动驾驶（AV）有望通过实现更快、更安全和更高效的响应来革新应急服务。这种转变由人工智能（AI）的进步推动，特别是强化学习（RL），使AV能够在复杂环境中导航并实时做出关键决策。然而，传统RL范式往往存在样本效率低且在动态应急场景中缺乏适应性的问题。本文回顾了下一代AV优化策略，以解决这些限制。我们分析了从传统RL向扩散模型（DM）增强的RL转变，通过合成数据生成提高策略鲁棒性，尽管计算成本增加。此外，我们还探讨了新兴的大型语言模型（LLM）辅助上下文学习（ICL）范式，它提供了一种轻量且可解释的替代方案，通过实现快速、即兴适应而无需重新训练。通过回顾AV智能、DM增强的RL和LLM辅助ICL的最新进展，本文为从生成式AI视角理解下一代自主应急响应系统提供了关键框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
