---
license: mit
task_categories:
- video-classification
- text-classification
- object-detection
language:
- en
tags:
- pedsimbench
- pedestrian-simulation
- autonomous-vehicles
- pedestrian-behavior
- behavior-prediction
- temporal-annotation
- video-understanding
pretty_name: >-
  DOPe: A Danger-Obsessed Pedestrian Dataset for Autonomous Vehicle Safety
  Testing 
size_categories:
- 1K<n<10K
---


# DOPe: A Danger-Obsessed Pedestrian Dataset for Autonomous Vehicle Safety Testing

## Dataset Description

**DOPe** is a comprehensive collection of real-world video annotations focused on pedestrian behavior in traffic scenarios. This dataset is specifically designed for autonomous vehicle research, particularly for understanding pedestrian decision-making, behavioral patterns, and critical interaction scenarios between pedestrians and vehicles.

The dataset contains frame-level annotations of pedestrian behaviors, vehicle responses, environmental contexts, and behavioral archetypes extracted from real-world traffic videos, making it invaluable for training and evaluating pedestrian prediction models, risk assessment systems, and autonomous vehicle decision-making algorithms.


## Use Cases

This dataset supports multiple research and development applications:

1. **Pedestrian Behavior Prediction**: Train models to anticipate pedestrian actions in traffic scenarios
2. **Risk Assessment**: Develop systems to evaluate collision risk based on pedestrian and vehicle behaviors
3. **Autonomous Vehicle Decision-Making**: Improve AV response strategies to various pedestrian behaviors
4. **Traffic Safety Analysis**: Study patterns and factors contributing to pedestrian-vehicle interactions
5. **Behavioral Archetype Recognition**: Classify pedestrian types (jaywalkers, distracted pedestrians, etc.)
6. **Multi-modal Learning**: Combine with video data for vision-based behavior understanding

## Dataset Structure

### Data Fields

Each row in the dataset represents a single annotated temporal segment with the following fields:

| Column | Type | Description |
|--------|------|-------------|
| `id` | integer | Unique identifier for each annotation |
| `video_path` | string | YouTube URL of the source video |
| `start_frame` | integer | Starting frame number of the annotated segment |
| `end_frame` | integer | Ending frame number of the annotated segment |
| `pedestrian_behavior_tags` | string | Comma-separated tags describing pedestrian behaviors |
| `vehicle_tags` | string | Comma-separated tags describing vehicle/ego behaviors |
| `environment_tags` | string | Comma-separated tags describing scene and environmental conditions |
| `archetypes` | string | Comma-separated behavioral archetype classifications |





### Source Data

The dataset is derived from real-world traffic videos, primarily sourced from YouTube, capturing authentic pedestrian-vehicle interactions across various:
- Geographic locations
- Traffic conditions
- Time periods (day/night)
- Road types and configurations
- Weather conditions

### Annotation Process

Each video segment was manually annotated by trained annotators who identified:
1. Temporal boundaries (start and end frames) of interaction events
2. Pedestrian behaviors observed during the segment
3. Vehicle responses and actions
4. Environmental and contextual factors
5. Behavioral archetype classifications

### Annotation Quality

- **Frame-level precision**: Annotations specify exact frame numbers for temporal accuracy
- **Multi-label approach**: Multiple tags can be assigned to capture complex behaviors
- **Contextual completeness**: Each annotation includes pedestrian, vehicle, environment, and archetype information


### Limitations

1. **Video Quality**: Source videos vary in resolution, frame rate, and quality
2. **Annotation Subjectivity**: Some behavioral interpretations may contain subjective elements
3. **Geographic Bias**: Dataset may over-represent certain regions or traffic cultures
4. **Scenario Coverage**: May not capture all possible pedestrian-vehicle interaction types
5. **Temporal Resolution**: Frame-level annotations dependent on source video FPS

## Technical Specifications

- **Frame Rate**: Typically 30 FPS (varies by source video)
- **Annotation Format**: CSV with comma-separated multi-label tags
- **Video Access**: Via YouTube URLs (requires internet connection and YouTube API access)
- **Recommended Processing**: Frame extraction from videos using provided frame numbers