# D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios

arXiv: https://arxiv.org/abs/1904.01975 · HTML: https://mlsanity.com/paper/1904.01975/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 28 of 28)

- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving](https://mlsanity.com/paper/2311.05332/) (2023)
- [GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control](https://mlsanity.com/paper/2412.11198/) (2024)
- [StreamForest: Efficient Online Video Understanding with Persistent Event Memory](https://mlsanity.com/paper/2509.24871/) (2025)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes](https://mlsanity.com/paper/2204.01026/) (2022)
- [BAAI-VANJEE Roadside Dataset: Towards the Connected Automated Vehicle Highway technologies in Challenging Environments of China](https://mlsanity.com/paper/2105.14370/) (2021)
- [DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos](https://mlsanity.com/paper/2405.17705/) (2024)
- [METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors](https://mlsanity.com/paper/2109.07648/) (2021)
- [D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights](https://mlsanity.com/paper/2207.10398/) (2022)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [FinnWoodlands Dataset](https://mlsanity.com/paper/2304.00793/) (2023)
- [A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches](https://mlsanity.com/paper/2401.14718/) (2024)
- [Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources](https://mlsanity.com/paper/2209.01750/) (2022)
- [Street Gaussians without 3D Object Tracker](https://mlsanity.com/paper/2412.05548/) (2024)
- [CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles](https://mlsanity.com/paper/2111.08243/) (2021)
- [SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring](https://mlsanity.com/paper/2507.11910/) (2025)
- [Object Recognition Datasets and Challenges: A Review](https://mlsanity.com/paper/2507.22361/) (2025)
- [Streaming Video Analytics On The Edge With Asynchronous Cloud Support](https://mlsanity.com/paper/2210.01402/) (2022)
- [Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba](https://mlsanity.com/paper/2504.03235/) (2025)
- [Artifacts of Idiosyncracy in Global Street View Data](https://mlsanity.com/paper/2505.11046/) (2025)
- [CrashChat: A Multimodal Large Language Model for Multitask Traffic Crash Video Analysis](https://mlsanity.com/paper/2512.18878/) (2025)
- [STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction](https://mlsanity.com/paper/2308.13183/) (2023)
- [Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning](https://mlsanity.com/paper/2311.07928/) (2023)
- [VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models](https://mlsanity.com/paper/2603.01454/) (2026)
- [Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation](https://mlsanity.com/paper/2605.00051/) (2026)
- [Object Detection for Autonomous Driving in Chinese Rural Scenes: An Experimental Study on Real-Synthetic Data Mixing and Model Evaluation](https://mlsanity.com/paper/2607.27058/) (2026)

## Abstract (arXiv)

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 D2-City, a large-scale comprehensive collection of dashcam videos collected by vehicles on DiDi's platform. D2-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, D2-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 D2-City dataset will advance the perception and related areas of intelligent driving.