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license: cc-by-4.0
pretty_name: DuckAD Driving Dataset
task_categories:
- robotics
- image-segmentation
tags:
- autonomous-driving
- imitation-learning
- end-to-end-driving
- carla
- duckietown
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: image
dtype: image
- name: seg
dtype: image
- name: bev
dtype: image
- name: command
dtype:
class_label:
names:
'0': DEFAULT
'1': LEFT
'2': STRAIGHT
'3': RIGHT
- name: speed
dtype: float32
- name: trajectory
list:
list: float32
length: 2
length: 10
- name: temporal_trajectory
list:
list: float32
length: 2
length: 10
- name: scenario
dtype: string
- name: map
dtype: string
- name: episode
dtype: string
- name: frame
dtype: int64
splits:
- name: train
num_bytes: 11667691286
num_examples: 214200
- name: test
num_bytes: 659226662
num_examples: 10200
download_size: 12310572997
dataset_size: 12326917948
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# DuckAD Driving Dataset
Expert driving demonstrations for **DuckAD**, an end-to-end vision-based driving model,
collected in [CARLA](https://carla.org) on custom Duckietown-style maps. A rule-aware expert
driver was rolled out under six traffic/obstacle scenarios; every frame pairs a front camera
image with the expert's future trajectory, a high-level navigation command, and ground-truth
bird's-eye-view (BEV) semantics.
- **214,200 training frames** from two maps (`duckietown_04`, `duckietown_05`), six scenarios
× 35,700 frames each.
- **10,200 test frames** from an *unseen* map (`duckietown_06`) rendered under *unseen* HDRI
lighting, for cross-map generalization evaluation.
- The simulator runs synchronously at 20 Hz and each timestep is captured from **three camera
rigs** (center, left, right — side rigs give recovery-style viewpoint diversity with
correspondingly transformed trajectory labels), so the training set corresponds to roughly
**one hour of expert driving**.
**Note that this dataset needs to be scaled 1/20 for the real Duckietown!**
## Scenarios
| `scenario` | Traffic | Duckies |
|---|---|---|
| `baseline` | – | – |
| `traffic_only` | ✓ | – |
| `ducks_roadside` | – | roadside |
| `ducks_roadside_traffic` | ✓ | roadside |
| `ducks_obstacle` | – | on-road obstacles |
| `ducks_obstacle_traffic` | ✓ | on-road obstacles |
## Fields
| Field | Type | Description |
|---|---|---|
| `image` | 224×224 RGB image | front (fisheye) camera frame |
| `seg` | 224×224 grayscale image | binary foreground mask: 255 = Duckietown foreground (road surface, lane markings, signs, bots, duckies), 0 = replaceable background. Used for background-swap augmentation. |
| `bev` | 64×64 grayscale image | ground-truth BEV semantics as raw CARLA semantic tag ids (see below) |
| `command` | class label | navigation command: `DEFAULT` (lane follow), `LEFT`, `STRAIGHT`, `RIGHT` (junction maneuvers) |
| `speed` | float32 | ego speed in m/s |
| `trajectory` | 10×2 float32 | future **spatial** waypoints at 1 m arc-length spacing, meters in the ego frame (x forward, y left) |
| `temporal_trajectory` | 10×2 float32 | future ego positions sampled every **0.3 s** (3.33 Hz), same ego frame — encodes the speed profile |
| `scenario` / `map` / `episode` / `frame` | strings / int | provenance metadata |
**BEV tag ids:** 29 `center_lane`, 30 `side_lane`, 31 `asphalt`, 32 `stop_lane`, 33 `sign`,
34 `bot`, 35 `duck`; any other id (e.g. 11 = terrain) is background. For training we remap
these to 8 contiguous classes (`background`=0 + the 7 above).
## Usage
```python
from datasets import load_dataset
ds = load_dataset("pamasan/duckad-data", split="train")
sample = ds[0]
sample["image"] # PIL.Image, 224x224 RGB front camera
sample["seg"] # PIL.Image, 224x224 foreground mask (255 = Duckietown foreground)
sample["bev"] # PIL.Image, 64x64 BEV semantics (CARLA tag ids, see table above)
sample["command"] # int class label: 0 DEFAULT, 1 LEFT, 2 STRAIGHT, 3 RIGHT
sample["speed"] # ego speed, m/s
sample["trajectory"] # 10 spatial waypoints [x, y], 1 m spacing, meters in ego frame
sample["temporal_trajectory"] # 10 future ego positions [x, y] sampled every 0.3 s -> speed profile
import numpy as np
bev_ids = np.array(sample["bev"]) # 64x64 tag ids
fg_mask = np.array(sample["seg"]) > 0 # boolean foreground mask
```
## Collection
Data was collected with a rule-aware expert (lane following, junction turns, stopping for
duckies and traffic) driving in synchronous CARLA at 20 Hz (`fixed_delta_seconds=0.05`).
Each recording segment respawns the ego at a new random location; traffic vehicles are
driven by the CARLA Traffic Manager. Junction approaches on the training maps are labeled
with the expert's chosen `LEFT`/`STRAIGHT`/`RIGHT` command; everywhere else the command is
`DEFAULT`.
## Notes
- Frames are stored as captured — **no augmentation is baked in**.
- The `test` split is for cross-map/lighting generalization; it uses a map and HDRI
environment that never appear in `train`.
## License
Released under **CC-BY 4.0**. All visible content (maps, duckiebot vehicles, props) is
custom-built for this dataset.
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