AD-Assurance-Lab/winter-driving-dataset

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

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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
└── raw/
    └── mcity_wspi/           # Raw recordings the synchronized CSVs were made from
        ├── csv/<day>/        # Per-topic ROS message exports, 62 runs over four days
        ├── jan20/            # Shakedown day: OxTS and vehicle CSVs, video, topic list
        ├── bags/             # The one surviving ROS 2 bag (jan20 highwaysouth1, 29 s)
        └── SHA256SUMS        # Hashes of all 712 raw files

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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

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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.

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Raw MCity-WSPI Recordings

The MCity-WSPI module (called Winter Wonderset in the SAE WCX paper describing it) was recorded on a Ford Mustang Mach-E at Mcity on 27, 28 and 29 January and 23 February 2026. The synchronized CSVs on GitHub are derived products. The recordings they were made from are under raw/mcity_wspi/:

Item	Content	Size
csv/<day>/	One CSV per ROS topic per run (oxts_imu, oxts_odometry, oxts_velocity, oxts_nav_sat_fix, vehicle_brake_info, vehicle_brake_report, vehicle_throttle_info, vehicle_throttle_report, vehicle_steering_report, vehicle_wheel_speeds, vehicle_wheel_positions) for 62 runs (jan27: 15, jan28: 13, jan29: 10, feb23: 24), plus the MARWIS log for jan27 and jan28. Each row is one message with its receive timestamp in nanoseconds, the header stamp and every field flattened	285 MB, 688 files
jan20/	The 20 January shakedown drives downtown1 and highwaysouth1: OxTS CSVs (imu, imu_bias, lever_arm, nav_sat_fix, nav_sat_ref, ncom, odometry), vehicle brake, wheel speed and position CSVs, a 29 s video, the bag metadata and topic list	22 MB
bags/	wmu-jan20-highwaysouth1_0.db3, the only ROS 2 bag that survived: 29 s, 68,319 messages, 50 topics including /arenacam6/images, /oxts/*, /vehicle/* (Dataspeed ds_dbw_msgs) and /can_bus_dbw/can_rx	3.5 GB

The bags for the January 27 to 29 and February 23 runs were lost; only these CSV exports and the camera frames survive. Raw run names carry a wmu- prefix (January) or a feb23-2026_ prefix with underscores; the public names drop them, so raw wmu-jan27-test2 is public jan27-test-2. The camera frames in images/mcity_wspi/ are the raw frames renamed frame_NNNNN.png in timestamp order; each frame's capture time is the timestamp_ns column of its row in the run's _sync.csv. See docs/raw-data.md on GitHub for the full map, and tools/wspi_sync.py to rebuild the synchronized CSVs from these exports.

Download one day's exports, or everything raw:

hf download AD-Assurance-Lab/winter-driving-dataset --repo-type dataset --include "raw/mcity_wspi/csv/jan27/*" --local-dir ./data
hf download AD-Assurance-Lab/winter-driving-dataset --repo-type dataset --include "raw/mcity_wspi/*" --local-dir ./data

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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}
}