DispatchRAG：基于交通事故视频中的现实世界协议进行应急调度决策
DispatchRAG: Grounding Emergency Dispatch Decisions in Real-World Protocols from Traffic Accident Video
- Graduate School of Information Science and Technology, University of Tokyo（东京大学信息科学与技术研究生院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究旨在通过DispatchRAG框架，利用基于RAG的检索机制和大型语言模型驱动的推理器，根据日本现实交通事故响应协议进行事故评估和调度，引入事故调度数据集验证框架，为自动驾驶车辆事故报告提供支持。
AI中文摘要：
评估交通事故场景的严重程度对于决定派遣哪种应急服务很重要。在行人事故中错过救护车派遣是一个致命问题。最近，视觉语言模型（VLM）是事故推理的有前途的工具，但许多VLM未基于现实生活中的事故响应协议，无法直接用于事故严重程度评估。我们引入了DispatchRAG，这是一个基于日本现实交通事故响应协议的事故评估和调度框架，旨在增强VLM在紧急情况下生成适当应急响应的能力。利用基于RAG的检索机制检索最相关的事故协议，并使用由大型语言模型驱动的推理器来建议最适当的响应。为支持评估，我们引入了事故调度数据集，这是一个根据日本事故响应协议改编自MM-AU数据集的事故评估和应急响应的综合数据集。我们在事故调度数据集上验证了我们的框架，与基线VLM相比，在各种事故场景中表现出强大的性能，表明可集成到能自动报告自身及附近事故的自动驾驶车辆中。
英文摘要：
Assessing the severity of a traffic accident scenario is important to decide which emergency service to dispatch. Missing an ambulance dispatch on a pedestrian accident is a fatal issue that can lead to death. Recently, Vision-Language Models (VLMs) have been a promising tool for accident reasoning, yet many VLMs are not grounded in real-life accident response protocols, making them not usable in accident severity assessment off-the-shelf. We introduced DispatchRAG, an accident assessor and dispatcher framework grounded in real-life Japanese traffic-accident response protocols, designed to enhance VLMs to generate an appropriate emergency response during an emergency scenario. Utilizing a RAG-based retrieval mechanism to retrieve the most relevant accident protocol and an LLM-powered reasoner to suggest the most proper response. To support evaluation, we introduce Accident Dispatch Dataset, a comprehensive dataset of accident assessment and emergency response according to Japanese accident response protocols adapted from the MM-AU dataset. We validate our framework on the Accident Dispatch Dataset, showing strong performance across various accident scenarios compared to the baseline VLM, pointing toward integration in autonomous vehicles that can automatically report both their own and nearby accidents.
