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README.md

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+# 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")
+```