Create README.md

README.md

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+---
+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](https://github.com/OpenDriveLab/DriveLM/tree/DriveLM-CARLA/pdm_lite). 
+
+## Dataset Statistics
+- **Diverse Scenarios:** Covers 38 complex scenarios, including urban traffic, participants violating traffic rules, and high-speed highway driving
+- **Focused Evaluation:** Short routes with 1 or 3 scenarios per route
+- **Data Types**: RGB images (.jpg), LiDAR point clouds (.laz), Sensor measurements (.json.gz), Bounding boxes (.json.gz), Commentary text, Dreamer model outputs
+
+## 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
+
+```bash
+# 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
+
+```bash
+# 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
+```bash
+# 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:
+```bibtex
+@inproceedings{sima2024drivelm,
+  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},
+}
+```