  As the roadside perception plays an increasingly significant role in the
Connected Automated Vehicle Highway(CAVH) technologies, there are immediate
needs of challenging real-world roadside datasets for bench marking and
training various computer vision tasks such as 2D/3D object detection and
multi-sensor fusion. In this paper, we firstly introduce a challenging
BAAI-VANJEE roadside dataset which consist of LiDAR data and RGB images
collected by VANJEE smart base station placed on the roadside about 4.5m high.
This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images,
including 20% collected at the same time. It also contains 12 classes of
objects, 74K 3D object annotations and 105K 2D object annotations. By providing
a real complex urban intersections and highway scenes, we expect the
BAAI-VANJEE roadside dataset will actively assist the academic and industrial
circles to accelerate the innovation research and achievement transformation in
the field of intelligent transportation in big data era.
