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AVD2: Accident Video Diffusion for Accident Video Description

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arxiv 2502.14801 v3 pith:XKTKQFS7 submitted 2025-02-20 cs.CV

Cheng Li , Keyuan Zhou , Tong Liu , Yu Wang , Mingqiao Zhuang , Huan-ang Gao , Bu Jin , Hao Zhao This is my paper · ORCID
classification cs.CV
keywords accidentvideoaccidentsavd2datasetdescriptiondiffusionemm-au
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    SafeMVDrive generates multi-view, real-world safety-critical driving videos by selecting adversarial vehicles with a GRPO-finetuned vision-language model and simulating collision-evasion trajectories.

  3. CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting

    cs.CV 2025-07 conditional novelty 5.0 of 10

    CRUISE reconstructs real V2X driving scenes as editable Gaussians, then shows that training on its generated data improves 3D detection and tracking on the V2X-Seq benchmark.

  4. Challenger: Affordable Adversarial Driving Video Generation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A framework for automatic generation of photorealistic adversarial driving videos, shown to sharply increase collision rates of end-to-end autonomous driving models.

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