构建首个面向自动驾驶未知场景的测评基准，提升系统应对突发危险能力。
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving
- 设计包含200+条行车记录视频的新型数据集，聚焦未标注物体与突发风险。
- 支持异常检测、开放集识别等多任务评估，覆盖真实道路复杂场景。
- 适合研究自动驾驶鲁棒性与开放世界学习的研究者使用。
随着计算机视觉技术在自动驾驶领域快速发展，更安全高效的无人驾驶交通系统正逐步实现。然而截至2024年，我们仍未拥有完全自动驾驶的汽车。其中核心挑战之一是处理新颖性问题——现有系统难以应对开放道路上未曾见过的状况。为此，我们提出了挑战未知标签（COOOL）基准，构建了一个全新的危险检测数据集，提供适用于多种任务的多样化评估指标，涵盖异常检测、开放集识别、开放词汇和域适应等邻近领域。COOOL包含超过200个以行车记录仪视角拍摄的视频集合，由人工标注者识别感兴趣目标与潜在驾驶风险。数据涵盖多样化的危险和干扰物。由于数据规模大且结构复杂，COOOL仅作为评估基准使用。
原文摘要 · Abstract (English)
As the Computer Vision community rapidly develops and advances algorithms for autonomous driving systems, the goal of safer and more efficient autonomous transportation is becoming increasingly achievable. However, it is 2024, and we still do not have fully self-driving cars. One of the remaining core challenges lies in addressing the novelty problem, where self-driving systems still struggle to handle previously unseen situations on the open road. With our Challenge of Out-Of-Label (COOOL) benchmark, we introduce a novel dataset for hazard detection, offering versatile evaluation metrics applicable across various tasks, including novelty-adjacent domains such as Anomaly Detection, Open-Set Recognition, Open Vocabulary, and Domain Adaptation. COOOL comprises over 200 collections of dashcam-oriented videos, annotated by human labelers to identify objects of interest and potential driving hazards. It includes a diverse range of hazards and nuisance objects. Due to the dataset's size and data complexity, COOOL serves exclusively as an evaluation benchmark.
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