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arXiv · 2507.02074

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

Sanjeda Akter·Ibne Farabi Shihab·Anuj Sharma
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Abstract

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.

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Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma. 2025-07-02. Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges. https://arxiv.org/abs/2507.02074

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