# Roadwork Cones Dataset

This dataset is designed for detecting roadwork-zone objects in autonomous driving scenarios. It contains three classes - traffic cones, roadworks signs, and vertical guide panels (delineators) - captured from four vehicle-mounted cameras across 39 driving sessions in urban and suburban roads.

## Dataset Description

- **Source:** Fleet of autonomous vehicles, urban and suburban roads
- **Sensor:** 4x LUCID TRI054S-CC cameras (2880×1860), front/side facing
- **Coverage:** ~31K bounding box annotations, ~4.7K unique images, 3 classes
- **Splits:** Train 70% (22,841 rows, 2,871 images), Test 30% (8,561 rows, 1,803 images) - split by driving session to prevent temporal leakage
- **Format:** Parquet (annotations) + JPEG (images) in `train/` and `test/` subdirectories

### Classes (3)

| Class | Count | Description |
|-------|-------|-------------|
| cone | 11,520 | Standard traffic cone |
| roadworks | 2,813 | Roadwork zone sign / panel |
| vertical_pannel | 17,069 | Vertical guide panel (delineator) |

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| bbox_msg_id | VARCHAR | UUID linking to the original bounding box message |
| object_id | BIGINT | Unique object tracking ID |
| label | VARCHAR | Object class (`cone`, `roadworks`, `vertical_pannel`) |
| bbox_coords | DOUBLE[4] | Bounding box [x, y, width, height] in pixel coordinates |
| timestamp_ns | BIGINT | ROS bag timestamp (nanoseconds) |
| image_path | VARCHAR | Relative path to JPEG in `train/` or `test/` |

### Data Splits

| Split | Rows | Images |
|-------|------|--------|
| train | 22,841 | 2,871 |
| test  | 8,561  | 1,803 |

### Dataset Structure

```
roadwork_cones_dataset/
├── annotations.parquet   # All annotations (31,402 rows)
├── train/
│   ├── camera_1C0FAF5250E2/   # 854 images
│   ├── camera_1C0FAF57D6F8/   # 1,415 images
│   ├── camera_1C0FAF5CA7B6/   # 494 images
│   └── camera_1C0FAF5CC14D/   # 108 images
└── test/
    ├── camera_1C0FAF5250E2/   # 570 images
    ├── camera_1C0FAF57D6F8/   # 1,193 images
    ├── camera_1C0FAF5CA7B6/   # 40 images
    └── camera_1C0FAF5CC14D/   # 0 images
```

### Usage

```python
import pandas as pd

df = pd.read_parquet("annotations.parquet")
print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")
```