  Intersection is one of the most challenging scenarios for autonomous driving
tasks. Due to the complexity and stochasticity, essential applications (e.g.,
behavior modeling, motion prediction, safety validation, etc.) at intersections
rely heavily on data-driven techniques. Thus, there is an intense demand for
trajectory datasets of traffic participants (TPs) in intersections. Currently,
most intersections in urban areas are equipped with traffic lights. However,
there is not yet a large-scale, high-quality, publicly available trajectory
dataset for signalized intersections. Therefore, in this paper, a typical
two-phase signalized intersection is selected in Tianjin, China. Besides, a
pipeline is designed to construct a Signalized INtersection Dataset (SIND),
which contains 7 hours of recording including over 13,000 TPs with 7 types.
Then, the behaviors of traffic light violations in SIND are recorded.
Furthermore, the SIND is also compared with other similar works. The features
of the SIND can be summarized as follows: 1) SIND provides more comprehensive
information, including traffic light states, motion parameters, High Definition
(HD) map, etc. 2) The category of TPs is diverse and characteristic, where the
proportion of vulnerable road users (VRUs) is up to 62.6% 3) Multiple traffic
light violations of non-motor vehicles are shown. We believe that SIND would be
an effective supplement to existing datasets and can promote related research
on autonomous driving.The dataset is available online via:
https://github.com/SOTIF-AVLab/SinD
