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---
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pretty_name: "Robusto-1 Extended"
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tags:
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- video
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- urban-driving
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- peru
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- traffic
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- autonomous-driving
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license: apache-2.0
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task_categories:
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- video-classification
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---
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# Robusto-1 Extended Dataset
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## Description
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The **Robusto-1 Extended** dataset contains videos recorded from 3 different cities in Peru: mainly Lima, Cusco and Cajamarca;
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along with detailed information about vehicle actions, external factors, and driving conditions.
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This dataset is useful for tasks such as vehicle behavior analysis, traffic condition detection, and driving scenario simulation.
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Each video record includes data on the ego-vehicle's behavior, external conditions (traffic signs, weather, etc.), and more.
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The videos were recorded under various traffic conditions, with each one labeled with its corresponding metadata.
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### Features:
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- **ID**: Unique identifier for the video.
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- **Video_Name**: Filename of the video.
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- **Place**: Location where the video was recorded.
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- **Ego_Vehicle**: Detailed information about the ego-vehicle’s actions during the recording.
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- **External_Factors**: External conditions such as traffic signs, weather, etc.
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- **Video**: Path to the video file in `.mp4` format.
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## Metadata Example
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Each video in the dataset is associated with metadata containing relevant information about the driving conditions at the time of recording. Below is an example of one metadata record:
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```json
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{
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"ID": "Robusto 1_1",
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"Place": "Lima",
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"Ego_Vehicle": {
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"Vehicle Actions": "Advancing Intermittently",
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"Driving Action Reasoning": "Following Vehicle Ahead",
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"Vehicle Motion Behavior": "Stop-and-Go"
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},
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"External_Factors": {
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"Traffic Signs": "",
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"Traffic Lights": "Red",
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"Weather Conditions": "Clear",
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"Road Surface Conditions": "",
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"Road Structures": "",
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"Static objects": "Buildings",
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"Other Vehicle Behaviors": "Cutting Off",
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"Pedestrian Behavior": "",
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"Unexpected Obstacles": "",
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"Emergency Situations": "",
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"Lighting Conditions": "Daylight",
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"Traffic Conditions": "Heavy Traffic",
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"Driving Environment": "Urban"
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}
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}
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```
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## Usage
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This dataset can be used for vehicle behavior analysis, improving autonomous driving algorithms, or training/testing computer vision models that require data on traffic and vehicle behavior in various conditions.
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### Load the Dataset
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To load this dataset from Hugging Face, you can use the following code:
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```python
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from datasets import load_dataset
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dataset = load_dataset("path/to/robusto-1_extended")
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```
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## File Structure
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The structure of the dataset files is as follows:
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```
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robusto-1_extended/
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├── README.md
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├── metadata.jsonl
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└── videos/
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├── Robusto 1_1.mp4
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├── Robusto 1_2.mp4
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└── ...
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```
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- `README.md`: Contains the dataset description and YAML metadata at the top.
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- `metadata.jsonl`: File storing video metadata in JSON Lines format.
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- `train/`: Directory containing all `.mp4` video files.
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## Short Q&A:
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**Q**: What is the main difference between the Robusto-1 Extended and the Robusto-1 dataset?
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**A**: They are both composed of small 5 second clips of 10 frames per seconds of out-of-distribution driving scenes in Peru. The main difference is that the Robusto-1
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dataset is composed of 7 video clips with both Human and Machine responses and video meta-data, while the Robusto-1 Extended is only
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composed of 200 videos with their meta-data to facilitate further research in VQA research so that different groups can create their own questions.
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## Citation
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If you use this dataset in your research, please cite our ArXiv pre-print that was recently accepted at the **CVPR 2025 Workshop on
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Distillation of Foundation Models for Autonomous Driving**.
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```
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@article{cusipuma2025robusto,
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title={Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru},
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author={Cusipuma, Dunant and Ortega, David and Flores-Benites, Victor and Deza, Arturo},
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journal={arXiv preprint arXiv:2503.07587},
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year={2025}
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}
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```
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```
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This `README.md` provides an overview, usage instructions, and metadata example, along with the necessary information to cite the dataset in academic work. |
