metadata

license: other
task_categories:
  - visual-question-answering
  - robotics
language:
  - en
tags:
  - AutonomousDriving
  - VQA
  - Commentary
  - VLA

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},
}