  Due to the high complexity and occlusion, insufficient perception in the
crowded urban intersection can be a serious safety risk for both human drivers
and autonomous algorithms, whereas CVIS (Cooperative Vehicle Infrastructure
System) is a proposed solution for full-participants perception in this
scenario. However, the research on roadside multimodal perception is still in
its infancy, and there is no open-source dataset for such scenario.
Accordingly, this paper fills the gap. Through an IPS (Intersection Perception
System) installed at the diagonal of the intersection, this paper proposes a
high-quality multimodal dataset for the intersection perception task. The
center of the experimental intersection covers an area of 3000m2, and the
extended distance reaches 300m, which is typical for CVIS. The first batch of
open-source data includes 14198 frames, and each frame has an average of 319.84
labels, which is 9.6 times larger than the most crowded dataset (H3D dataset in
2019) by now. In order to facilitate further study, this dataset tries to keep
the label documents consistent with the KITTI dataset, and a standardized
benchmark is created for algorithm evaluation. Our dataset is available at:
http://www.openmpd.com/column/other_datasets.
