| # Roadwork Cones Dataset |
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| 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. |
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|
| ## Dataset Description |
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|
| - **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 |
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|
| ### Classes (3) |
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|
| | 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 |
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|
| ```python |
| import pandas as pd |
| |
| df = pd.read_parquet("annotations.parquet") |
| print(f"{len(df)} annotations, {df.image_path.nunique()} unique images") |
| ``` |
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