METEOR：一个具有罕见行为的密集、异构和非结构化流量数据集
METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors
摘要：
我们提出了一个新的交通数据集，METEOR，它捕捉交通模式和多智能体驾驶行为在非结构化的场景。METEOR包含超过1000个一分钟的视频，超过200万个带有边界框和GPS轨迹的注释帧，用于16个独特的代理类别，以及超过1300万个交通代理边界框。METEOR是一个罕见而有趣的多智能体驾驶行为数据集，这些行为分为交通违规，非典型交互和不同场景。METEOR中的每个视频都使用各种因素进行标记，这些因素对应于天气，一天中的时间，道路状况和交通密度。我们使用METEOR基准感知方法的目标检测和多智能体行为预测。我们的关键发现是，最先进的目标检测和行为预测模型在Waymo等现有数据集上取得了成功，但在METEOR数据集上却失败了。METEOR标志着为密集、异构和非结构化场景开发更复杂的感知模型的第一步。
英文摘要：
We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios. METEOR consists of more than 1000 one-minute videos, over 2 million annotated frames with bounding boxes and GPS trajectories for 16 unique agent categories, and more than 13 million bounding boxes for traffic agents. METEOR is a dataset for rare and interesting, multi-agent driving behaviors that are grouped into traffic violations, atypical interactions, and diverse scenarios. Every video in METEOR is tagged using a diverse range of factors corresponding to weather, time of the day, road conditions, and traffic density. We use METEOR to benchmark perception methods for object detection and multi-agent behavior prediction. Our key finding is that state-of-the-art models for object detection and behavior prediction, which otherwise succeed on existing datasets such as Waymo, fail on the METEOR dataset. METEOR marks the first step towards the development of more sophisticated perception models for dense, heterogeneous, and unstructured scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
