用扩散模型生成事故视频，帮助理解事故原因并预防
AVD2: Accident Video Diffusion for Accident Video Description
- 基于扩散模型生成与描述匹配的事故视频
- 构建了包含10万+样本的多模态事故数据集EMM-AU
- 提升事故分析与预防效果，适合自动驾驶研究者
交通意外对自动驾驶构成复杂挑战，常因场景不可预测而难以准确解析与响应。现有方法因缺乏事故场景训练数据，难以揭示事故成因或提出预防措施。本文提出AVD2（事故视频扩散生成用于事故视频描述），通过生成与自然语言描述和推理一致的事故视频，构建了增强型多模态事故视频理解数据集EMM-AU。实验表明，融合EMM-AU数据集在自动评估与人工评价中均达到当前最佳性能，显著推动事故分析与预防领域发展。项目资源见https://an-answer-tree.github.io
原文摘要 · Abstract (English)
Traffic accidents present complex challenges for autonomous driving, often featuring unpredictable scenarios that hinder accurate system interpretation and responses. Nonetheless, prevailing methodologies fall short in elucidating the causes of accidents and proposing preventive measures due to the paucity of training data specific to accident scenarios. In this work, we introduce AVD2 (Accident Video Diffusion for Accident Video Description), a novel framework that enhances accident scene understanding by generating accident videos that aligned with detailed natural language descriptions and reasoning, resulting in the contributed EMM-AU (Enhanced Multi-Modal Accident Video Understanding) dataset. Empirical results reveal that the integration of the EMM-AU dataset establishes state-of-the-art performance across both automated metrics and human evaluations, markedly advancing the domains of accident analysis and prevention. Project resources are available at https://an-answer-tree.github.io
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。