构建大规模驾驶场景动作感知数据集，助力自动驾驶理解交通参与者行为。
ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving
- 基于Waymo数据构建，标注198万帧视频与1240万标签。
- 包含54000个目标轨迹、390万边界框，覆盖多城市真实场景。
- 支持跨国家域适应研究，适配自动驾驶算法训练与评测。
自动驾驶感知系统不仅需要识别物体或分割场景，更需全面理解场景中发生的行为以实现安全交互。目前缺乏专门用于训练和开发交通参与者行为理解算法的数据集。本文提出ROAD-Waymo，一个面向道路场景中智能体、行为、位置与事件检测的大型数据集，作为美国Waymo开放数据集的扩展层。该数据集规模远超现有同类数据集，涵盖多个城市，包含198万标注视频帧、5.4万条智能体轨迹、390万边界框及总计1240万条标签。通过专为本数据集设计的新型自动标注校验流程，确保了数据完整性与质量。由于兼容英国原始ROAD数据集，ROAD-Waymo可构建新的跨国家域适应基准——ROAD++，推动不同国家真实道路场景间的算法迁移研究。
原文摘要 · Abstract (English)
Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to comprehend the actions of other road users. This paper presents ROAD-Waymo, an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes, provided as a layer upon the (US) Waymo Open dataset. Considerably larger and more challenging than any existing dataset (and encompassing multiple cities), it comes with 198k annotated video frames, 54k agent tubes, 3.9M bounding boxes and a total of 12.4M labels. The integrity of the dataset has been confirmed and enhanced via a novel annotation pipeline designed for automatically identifying violations of requirements specifically designed for this dataset. As ROAD-Waymo is compatible with the original (UK) ROAD dataset, it provides the opportunity to tackle domain adaptation between real-world road scenarios in different countries within a novel benchmark: ROAD++.
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