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

TL;DR

D$^2$-City is a large-scale, diverse dashcam video dataset with extensive annotations, designed to enhance research in intelligent driving by reflecting real-world traffic complexities in China.

Contribution

The paper introduces D$^2$-City, a comprehensive dataset with over 10,000 videos, detailed annotations, and diverse scenarios to support advancements in autonomous driving research.

Findings

Contains over 10,000 dashcam videos reflecting real-world traffic diversity.

Provides detailed bounding box and tracking annotations for 12 object classes.

Includes data across various weather, road, and traffic conditions.

Abstract

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.