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
Dataset Structure
This repository is structured to separate tabular metadata, pre-trained models, and large image binaries:
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.
1. Download Specific Run Images
To download and automatically unzip a specific run package, use the download script:
python3 tools/download_wspi.py --run jan27-downtown-1_images --dest_dir ./data
2. Download Pre-trained Lane Models
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:
@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}
}