Datasets:

AD-Assurance-Lab
/

winter-driving-dataset

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label mcity_wspi
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2365, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2282, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label mcity_wspi

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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}
}

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