---
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}
}
```