← 研踪 PaperTrailarXiv:2507.02074 · v3
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
首次提交 2025-07-03 02:21
版本更新 2026-11-02 06:34
雷达观测 2026-11-04 23:42
摘要
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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v3 · 2026-11-02 06:34 · 最新观测版本
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
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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