| --- |
| pretty_name: AD Winter Driving Dataset |
| task_categories: |
| - image-segmentation |
| - object-detection |
| tags: |
| - autonomous-driving |
| - lane-detection |
| - vehicle-dynamics |
| - weather-telemetry |
| license: cc-by-4.0 |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # AD Winter Driving Dataset: Multimodal Perception & Physics |
|
|
| This is the official Hugging Face repository hosting the image binary packages and pre-trained benchmarks for the **AD Winter Driving Dataset**, created by the **AD Assurance Lab** in partnership with **Western Michigan University** and **MCity**. |
|
|
| For the code repositories, synchronized CSV metadata, loaders, and developer documentation, please visit the main GitHub repository: |
| 👉 [GitHub: AD-Assurance-Lab/winter-driving-dataset](https://github.com/AD-Assurance-Lab/winter-driving-dataset) |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| This repository is structured to separate tabular metadata, pre-trained models, and large image binaries: |
|
|
| ```text |
| winter-driving-dataset/ |
| ├── metadata/ # Tabular metadata and annotations (on GitHub) |
| ├── models/ # Pre-trained lane detection checkpoints |
| │ ├── onnx/ # ONNX float32/float16 format checkpoints |
| │ └── pytorch/ # PyTorch .pt / .pth checkpoints |
| └── images/ |
| ├── mcity_wspi/ # Individual zip packages for each dynamics sequence |
| │ ├── jan27-downtown-1_images.zip |
| │ ├── feb23-straight-10_images.zip |
| │ └── ... |
| └── reva_perception/ |
| ├── labeled_set_images.zip # Labeled TuSimple-format training images |
| └── non_labeled_set/ # Unlabeled sequence frames |
| ``` |
|
|
| --- |
|
|
| ## How to Download and Load the Dataset |
|
|
| Instead of downloading this entire dataset manually, we provide dedicated Python tools inside the [GitHub Repository](https://github.com/AD-Assurance-Lab/winter-driving-dataset). |
|
|
| ### 1. Download Specific Run Images |
| To download and automatically unzip a specific run package, use the download script: |
| ```bash |
| python3 tools/download_wspi.py --run jan27-downtown-1_images --dest_dir ./data |
| ``` |
|
|
| ### 2. Download Pre-trained Lane Models |
| ```bash |
| python3 tools/download_models.py --model all --dest_dir ./models |
| ``` |
|
|
| --- |
|
|
| ## Aligned Physics Variables |
|
|
| Each run contains synchronized camera frames at 25Hz aligned with: |
| - **Vehicle Dynamics**: CAN bus steering angle, throttle/brake inputs, brake torque, and wheel speeds (fl, fr, rl, rr in rad/s). |
| - **Inertial Response**: High-grade 6-DOF IMU rotational rates and linear accelerations (including vertical ride response). |
| - **GPS/RTK**: Precise geographical coordinate trajectories. |
| - **Mobile Weather (MARWIS)**: Real-time optical road condition classifications and pavement friction estimations. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this dataset in your research, please cite the following: |
|
|
| ```bibtex |
| @article{tye2026misnow1000, |
| title={MI-Snow1000: A Comprehensive Dataset and Benchmark for Lane Detection in Adverse Winter Conditions}, |
| author={Tye, Eugene and Clinton, Catherine and Asher, Zachary and Fong, Alvis}, |
| journal={SAE International Journal of Connected and Autonomous Vehicles}, |
| volume={9}, |
| number={2}, |
| pages={112--128}, |
| year={2026}, |
| publisher={SAE International} |
| } |
| ``` |
|
|
