Pillars Dataset
This dataset is designed for detecting roadside vertical structures - pillars, poles, and pylons - which are critical static obstacles for autonomous vehicle navigation. It contains 12K bounding box annotations from two side/rear-facing cameras across 6 driving sessions in urban and suburban environments.
Dataset Description
- Source: Fleet of autonomous vehicles, urban and suburban roads
- Sensor: 2x LUCID TRI054S-CC cameras (2880×1860), front/side facing
- Coverage: ~12K bounding box annotations, ~1.4K unique images, single class
- Splits: Train 70% (11,080 rows, 1,242 images), Test 30% (1,035 rows, 139 images) - split by driving session to prevent temporal leakage
- Format: Parquet (annotations) + JPEG (images) in
train/ and test/ subdirectories
Classes (1)
| Class |
Count |
Description |
| pillar |
12,115 |
Roadside pillar / pole / pylon |
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 (pillar) |
| 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 |
11,080 |
1,242 |
| test |
1,035 |
139 |
Dataset Structure
pillars_dataset/
├── annotations.parquet # All annotations (12,115 rows)
├── train/
│ ├── camera_1C0FAF5250E2/ # 41 images
│ └── camera_1C0FAF57D6F8/ # 1,201 images
└── test/
├── camera_1C0FAF5250E2/ # 40 images
└── camera_1C0FAF57D6F8/ # 99 images
Usage
import pandas as pd
df = pd.read_parquet("annotations.parquet")
print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")
