Traffic conflicts have been studied by the transportation research community as a surrogate safety measure for decades. However, due to the rarity of traffic conflicts, collecting large-scale real-world traffic conflict data becomes extremely challenging. In this paper, we introduce and analyze ROCO - a real-world roundabout traffic conflict dataset. The data is collected at a two-lane roundabout at the intersection of State St. and W. Ellsworth Rd. in Ann Arbor, Michigan. We use raw video dataflow captured from four fisheye cameras installed at the roundabout as our input data source. We adopt a learning-based conflict identification algorithm from video to find potential traffic conflicts, and then manually label them for dataset collection and annotation. In total 557 traffic conflicts and 17 traffic crashes are collected from August 2021 to October 2021. We provide trajectory data of the traffic conflict scenes extracted using our roadside perception system. Taxonomy based on traffic conflict severity, reason for the traffic conflict, and its effect on the traffic flow is provided. With the traffic conflict data collected, we discover that failure to yield to circulating vehicles when entering the roundabout is the largest contributing reason for traffic conflicts. ROCO dataset will be made public in the short future.

翻译：几十年来，交通冲突一直作为替代安全指标被交通研究领域广泛探讨。然而，由于交通冲突的罕见性，大规模真实世界交通冲突数据的收集变得极具挑战性。本文介绍并分析了ROCO——一个真实世界的环岛交通冲突数据集。数据采集于密歇根州安娜堡市State St.与W. Ellsworth Rd.交叉口处的一个双车道环岛。我们利用安装在环岛上的四个鱼眼摄像头采集的原始视频数据流作为输入数据源。采用基于学习的视频冲突识别算法来发现潜在交通冲突，随后通过人工标注进行数据集收集与注释。从2021年8月至2021年10月，共收集到557起交通冲突和17起交通事故。我们提供了利用路侧感知系统提取的交通冲突场景轨迹数据，并依据交通冲突严重程度、冲突原因及其对交通流的影响建立了分类体系。通过收集的交通冲突数据，我们发现进入环岛时未让行环内行驶车辆是导致交通冲突的最主要因素。ROCO数据集将在近期公开发布。