  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.
