mlsanityData to 14 Oct 2026

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges

arXiv 2507.02074 ↗First posted 2 Jul 2025cs.CV cs.AIcited by 3

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Abstract (arXiv)

Crash detection from video feeds is a critical problem in intelligent transportation systems. Recent developments in large language models (LLMs) and vision-language models (VLMs) have transformed how we process, reason about, and summarize multimodal information. This paper surveys recent methods leveraging LLMs for crash detection from video data. We present a structured taxonomy of fusion strategies, summarize key datasets, analyze model architectures, compare performance benchmarks, and discuss ongoing challenges and opportunities. Our review provides a foundation for future research in this fast-growing intersection of video understanding and foundation models.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 59 of its 106 references that are in the map. First posted on arXiv 2 Jul 2025.

Builds on

59 of 106 references.

References · 59

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determinationcited by 1
  2. 2025Temporal Zoom Networks: Distance Regression and Continuous Depth for Efficient Action Localizationcited by 0
  3. 2026Automating Crash Diagram Generation Using Vision-Language Models: A Case Study on Multi-Lane Roundaboutscited by 0

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