Datasets:

RenzKa
/

simlingo

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<<DISTANCE>: string, <OBJECT>: string>
to
{'<OBJECT>': Value(dtype='string', id=None)}
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2270, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1888, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2220, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2220, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2006, in cast_array_to_feature
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2007, in <listcomp>
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1798, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<<DISTANCE>: string, <OBJECT>: string>
              to
              {'<OBJECT>': Value(dtype='string', id=None)}

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SimLingo Dataset

Overview

SimLingo-Data is a large-scale autonomous driving CARLA 2.0 dataset containing sensor data, action labels, a wide range of simulator state information, and language labels for VQA, commentary and instruction following. The driving data is collected with the privileged rule-based expert PDM-Lite.

Dataset Statistics

Large-scale dataset: 3,308,315 total samples (note: these are not from unique routes as the provided CARLA route files are limited)
Diverse Scenarios: Covers 38 complex scenarios, including urban traffic, participants violating traffic rules, and high-speed highway driving
Focused Evaluation: Short routes with 1 scenario (62.1%) or 3 scenarios (37.9%) per route
Data Types: RGB images (.jpg), LiDAR point clouds (.laz), Sensor measurements (.json.gz), Bounding boxes (.json.gz), Language annotations (.json.gz)

Dataset Structure

The dataset is organized hierarchically with the following main components:

data/: Raw sensor data (RGB, LiDAR, measurements, bounding boxes)
commentary/: Natural language descriptions of driving decisions
dreamer/: Instruction following data with multiple instruction/action pairs per sample
drivelm/: VQA data, based on DriveLM

Data Details

RGB Images: 1024x512 front-view camera image
Augmented RGB Images: 1024x512 front-view camera image with a random shift and orientation offset of the camera
LiDAR: Point cloud data saved in LAZ format
Measurements: Vehicle state, simulator state, and sensor readings in JSON format
Bounding Boxes: Detailed information about each object in the scene.
Commentary, Dreamer, VQA: Language annotations

Usage

This dataset is chunked into groups of multiple routes for efficient download and processing.

Download the whole dataset using git with Git LFS

# Clone the repository
git clone https://huggingface.co/datasets/RenzKa/simlingo

# Navigate to the directory
cd simlingo

# Pull the LFS files
git lfs pull

Download a single file with wget

# Download individual files (replace with actual file URLs from Hugging Face)
wget https://huggingface.co/datasets/RenzKa/simlingo/resolve/main/[filename].tar.gz

Extract to a single directory - please specify the location where you want to store the dataset

# Create output directory
mkdir -p database/simlingo

# Extract all archives to the same directory
for file in *.tar.gz; do
    echo "Extracting $file to database/simlingo/..."
    tar -xzf "$file" -C database/simlingo/
done

License

Please refer to the license file for usage terms and conditions.

Citation

If you use this dataset in your research, please cite:

@inproceedings{renz2025simlingo,
  title={SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment},
  author={Renz, Katrin and Chen, Long and Arani, Elahe and Sinavski, Oleg},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2025},
}
@inproceedings{sima2024drivelm,
  title={DriveLM: Driving with Graph Visual Question Answering},
  author={Chonghao Sima and Katrin Renz and Kashyap Chitta and Li Chen and Hanxue Zhang and Chengen Xie and Jens Beißwenger and Ping Luo and Andreas Geiger and Hongyang Li},
  booktitle={European Conference on Computer Vision},
  year={2024},
}

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