Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
It addresses the problem of crash detection for intelligent transportation systems, but it is incremental as it is a survey paper.
This paper surveys recent methods that use large language models for crash detection in video, summarizing fusion strategies, datasets, and performance benchmarks to provide a foundation for future research.
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.