解决行车记录仪中未知危险的检测难题，三任务一体应对。
Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL Challenge
- 用速度与声音异常检测司机反应，无监督学习不依赖标注。
- 启发式规则+集成方法识别危险物体，差分隐私防过拟合。
- 视觉语言模型生成描述性标签，适合自动驾驶安全研究者。
本文提出一种新型方法，用于行车记录仪视频中的危险分析，涵盖对驾驶员反应的检测、危险物体的识别以及描述性标题的生成。首先，通过速度与声音异常检测结合无监督学习，实现对驾驶员反应的识别；其次，采用一组启发式规则作为弱分类器，利用集成方法进行融合，并引入差分隐私技术以缓解过拟合问题，增强在缺乏标注数据下的鲁棒性；最后，使用先进的视觉-语言模型完成危险场景的描述性标注。该方法在自动驾驶中“非标签外”危险检测挑战赛（COOOL Challenge）中取得三项任务最高分，验证了其有效性。源代码已公开于 https://github.com/ffyyytt/COOOL_2025。
原文摘要 · Abstract (English)
This paper presents a novel approach for hazard analysis in dashcam footage, addressing the detection of driver reactions to hazards, the identification of hazardous objects, and the generation of descriptive captions. We first introduce a method for detecting driver reactions through speed and sound anomaly detection, leveraging unsupervised learning techniques. For hazard detection, we employ a set of heuristic rules as weak classifiers, which are combined using an ensemble method. This ensemble approach is further refined with differential privacy to mitigate overconfidence, ensuring robustness despite the lack of labeled data. Lastly, we use state-of-the-art vision-language models for hazard captioning, generating descriptive labels for the detected hazards. Our method achieved the highest scores in the Challenge on Out-of-Label in Autonomous Driving, demonstrating its effectiveness across all three tasks. Source codes are publicly available at https://github.com/ffyyytt/COOOL_2025.
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