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