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Driving datasets accelerate the development of intelligent driving andrelated computer vision technologies, while substantial and detailedannotations serve as fuels and powers to boost the efficacy of such datasets toimprove learning-based models. We propose D$^2$-City, a large-scalecomprehensive collection of dashcam videos collected by vehicles on DiDi'splatform. D$^2$-City contains more than 10000 video clips which deeply reflectthe diversity and complexity of real-world traffic scenarios in China. We alsoprovide bounding boxes and tracking annotations of 12 classes of objects in allframes of 1000 videos and detection annotations on keyframes for the remainderof the videos. Compared with existing datasets, D$^2$-City features data invarying weather, road, and traffic conditions and a huge amount of elaboratedetection and tracking annotations. By bringing a diverse set of challengingcases to the community, we expect the D$^2$-City dataset will advance theperception and related areas of intelligent driving.

D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios