DispatchRAG: Grounding Emergency Dispatch Decisions in Real-World Protocols from Traffic Accident Video
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 25 Jul 2026.
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15 of 26 references.
References · 15
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- 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
- 2021SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Eventscited by 84
- 2024Abductive Ego-View Accident Video Understanding for Safe Driving Perceptioncited by 43
- 2025VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understandingcited by 20
- 2023AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Modelcited by 17
- 2024GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Eventscited by 16
- 2024ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understandingcited by 13
- 2024Driving with Regulation: Trustworthy and Interpretable Decision-Making for Autonomous Driving with Retrieval-Augmented Reasoningcited by 13
3 more
- 2025SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understandingcited by 6
- 2026CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoningcited by 4
- 2025DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical servicescited by 2
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- 2025When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis
- 2024Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference
- 2026AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models
- 2026Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies
- 2024Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses
- 2026TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination
- 2025Hierarchical Reasoning with Vision-Language Models for Incident Reports from Dashcam Videos
- 2026TransportAgents: a multi-agents LLM framework for traffic accident severity prediction