用生成式AI提升自动驾驶应急响应的决策效率与适应性。
Advancing Autonomous Emergency Response Systems: A Generative AI Perspective
- 引入扩散模型生成合成数据，增强强化学习策略鲁棒性。
- 采用大语言模型实现无需重训练的快速场景适配。
- 适合关注智能交通与应急系统创新的研究者参考。
自动驾驶车辆（AV）有望通过更快、更安全、更高效的响应，彻底改变应急服务。这一变革由人工智能（AI）进步推动，尤其是强化学习（RL），使AV能够在复杂环境中实时导航并做出关键决策。然而，传统RL方法常面临样本效率低和动态应急场景下适应性差的问题。本文综述下一代AV优化策略，分析从传统RL向扩散模型（DM）增强型RL的转变，该方法通过合成数据生成提升策略鲁棒性，但计算开销增加。同时探讨大语言模型（LLM）辅助的上下文学习（ICL）新兴范式，其通过无需重训练的即时适应，提供轻量且可解释的替代方案。本文从生成式AI视角，系统梳理了AV智能、DM增强型RL与LLM辅助ICL的最新进展，为下一代自主应急响应系统构建了关键分析框架。
原文摘要 · Abstract (English)
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.
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