  Driving datasets accelerate the development of intelligent driving and
related computer vision technologies, while substantial and detailed
annotations serve as fuels and powers to boost the efficacy of such datasets to
improve learning-based models. We propose D$^2$-City, a large-scale
comprehensive collection of dashcam videos collected by vehicles on DiDi's
platform. D$^2$-City contains more than 10000 video clips which deeply reflect
the diversity and complexity of real-world traffic scenarios in China. We also
provide bounding boxes and tracking annotations of 12 classes of objects in all
frames of 1000 videos and detection annotations on keyframes for the remainder
of the videos. Compared with existing datasets, D$^2$-City features data in
varying weather, road, and traffic conditions and a huge amount of elaborate
detection and tracking annotations. By bringing a diverse set of challenging
cases to the community, we expect the D$^2$-City dataset will advance the
perception and related areas of intelligent driving.
