Datasets:

JakeWatson
/

UdacityCarSimDataset

---
license: gpl-3.0
language:
- en
pretty_name: Udacity Car Simulator Test Track
size_categories:
- 10K<n<100K
tags:
- vision
- Udacity
- ComputerVision
- DeepLearning
- CNN
- PyTorch
---
# Udacity Car Simulator Dataset

A preprocessed end-to-end driving dataset collected from the [Udacity Self-Driving Car Simulator](https://github.com/udacity/self-driving-car-sim), intended for training a CNN to predict steering angles from camera input.

Collected via manual mouse driving in the simulator and processed into NumPy arrays for fast loading during training. This dataset is used by the [University of Brighton RO501 Coursework project](https://github.com/Jake1402/University-of-Brighton-RO501-Coursework).

---

## Dataset Structure

The dataset consists of four `.npy` files:

| File | Shape | Dtype | Description |
|------|-------|-------|-------------|
| `center.npy` | `(N, 3, 160, 80)` | `float32` | Centre camera frames |
| `left.npy` | `(N, 3, 160, 80)` | `float32` | Left camera frames |
| `right.npy` | `(N, 3, 160, 80)` | `float32` | Right camera frames |
| `steer.npy` | `(M,)` | `float32` | Smoothed steering angles |

Where:
- **N** ≈ 27,000 — number of image frames per camera view
- **M** ≈ 54,000 — number of steering samples (M = 2 × N)
- Images are in **channel-first** format: `(batch, channels, height, width)`
- Color space is **HSV**, pixel values normalised to **[0, 1]**
- Steering values are in the range **[-1, 1]** (negative = left, positive = right)

> **Why M = 2 × N?** The raw simulator log records one row per frame with all three camera paths. During preprocessing, images are sampled every other row (`[::2]`) to reduce redundancy, while the steering signal is kept at full resolution and smoothed. See [Alignment](#aligning-images-and-steering) below for how to pair them up.

---

## Preprocessing Pipeline

The raw data starts as a `driving_log.csv` produced by the simulator with columns:

```
C_img, L_img, R_img, Steer, Accel, Deccel, Velocity
```

Each image goes through the following transformations before being saved:

1. **Crop** — rows `60:135` are kept (the top of the frame contains no useful driving information)
2. **Resize** — to `160 × 80` pixels
3. **Color space** — converted from BGR to HSV
4. **Normalise** — divided by 255 to give values in `[0, 1]`
5. **Reshape** — to channel-first `(3, 160, 80)`

The steering column is smoothed with a **moving average (window = 3)** to remove sudden jerks from manual driving.

---

## Loading the Dataset

### Option 1: Download files directly

Download all four `.npy` files into a local folder (e.g. `npysets/`):

```bash
mkdir npysets
huggingface-cli download JakeWatson/UdacityCarSimDataset --local-dir npysets
```

Or use the `datasets` library:

```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="JakeWatson/UdacityCarSimDataset",
    local_dir="npysets",
    allow_patterns=["*.npy"]
)
```

### Option 2: Load with NumPy

```python
import numpy as np
center = np.load("npysets/center.npy")   # (N, 3, 160, 80) float32
left   = np.load("npysets/left.npy")     # (N, 3, 160, 80) float32
right  = np.load("npysets/right.npy")    # (N, 3, 160, 80) float32
steer  = np.load("npysets/steer.npy")    # (M,) float32
print(center.shape)  # (27xxx, 3, 160, 80)
print(steer.shape)   # (54xxx,)
```

---

## Aligning Images and Steering

Because images were sampled at half the rate of steering, you need to subsample both arrays to align them. The companion project uses:

- **Images:** every 3rd frame → `arr[::3]`
- **Steering:** every 6th value → `arr[::6]`

This gives matching lengths for each camera view:

```python
X_center = np.load("npysets/center.npy")[::3]   # (N/3, 3, 160, 80)
X_left   = np.load("npysets/left.npy")[::3]     # (N/3, 3, 160, 80)
X_right  = np.load("npysets/right.npy")[::3]    # (N/3, 3, 160, 80)
y        = np.load("npysets/steer.npy")[::6]    # (N/3,)
assert X_center.shape[0] == y.shape[0]
```

---

## Building a Training Set

The standard approach (used in the companion project) concatenates all three camera views and offsets the steering label for left/right views to act as data augmentation:

```python
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader
def build_dataset(path="npysets", split=0.95, balance=(31, 550)):
    # Load and align
    X_center = np.load(f"{path}/center.npy")[::3]
    X_left   = np.load(f"{path}/left.npy")[::3]
    X_right  = np.load(f"{path}/right.npy")[::3]
    y        = np.load(f"{path}/steer.npy")[::6]
    # Concatenate views; offset steering for left/right cameras
    X = np.concatenate([X_center, X_left, X_right], axis=0)
    y = np.clip(np.concatenate([y, y + 0.35, y - 0.35]), -1.0, 1.0)
    # Balance by steering frequency (optional but recommended)
    bins, max_per_bin = balance
    bin_edges = np.histogram_bin_edges(y, bins=bins)
    bin_ids = np.digitize(y, bin_edges) - 1
    keep = []
    for b in range(bins):
        idx = np.where(bin_ids == b)[0]
        if len(idx) > max_per_bin:
            idx = np.random.choice(idx, max_per_bin, replace=False)
        keep.append(idx)
    keep = np.concatenate(keep)
    X, y = X[keep], y[keep]
    # Shuffle and split
    perm = np.random.permutation(X.shape[0])
    X, y = X[perm], y[perm]
    split_idx = int(X.shape[0] * split)
    train_ds = TensorDataset(
        torch.from_numpy(X[:split_idx].astype(np.float32)),
        torch.from_numpy(y[:split_idx].astype(np.float32))
    )
    test_ds = TensorDataset(
        torch.from_numpy(X[split_idx:].astype(np.float32)),
        torch.from_numpy(y[split_idx:].astype(np.float32))
    )
    return train_ds, test_ds
train_ds, test_ds = build_dataset()
# Create DataLoaders
train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)
test_loader  = DataLoader(test_ds, batch_size=32, shuffle=False)
```

### Why the ±0.35 offset?

The left and right cameras see the road shifted relative to the centre camera. Adding a constant to their steering labels teaches the model that "if I'm looking from the left camera, I need to steer slightly more right to stay centred." This is a form of view-based data augmentation that improves generalisation.

---

## Model Input/Output

The companion CNN (called **UBAC**) expects:

- **Input:** `(batch_size, 3, 160, 80)` — float32 tensor, HSV, normalised [0, 1]
- **Output:** `(batch_size, 1)` — steering angle in `[-1, 1]` (Tanh activation)

```python
import torch
from EffecientRes import ConvolutionalAgent  # from the companion repo
model = ConvolutionalAgent(action_space=1)
x = torch.randn(4, 3, 160, 80)  # batch of 4 images
steering = model(x)              # shape: (4, 1)
```

---

## Quick Start (Full Example)

```python
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader
from torch import nn
# 1. Load data
center = np.load("npysets/center.npy")[::3]
left   = np.load("npysets/left.npy")[::3]
right  = np.load("npysets/right.npy")[::3]
steer  = np.load("npysets/steer.npy")[::6]
# 2. Build augmented dataset
X = np.concatenate([center, left, right], axis=0).astype(np.float32)
y = np.clip(np.concatenate([steer, steer + 0.35, steer - 0.35]), -1, 1).astype(np.float32)
# 3. Shuffle & split
perm = np.random.permutation(X.shape[0])
X, y = X[perm], y[perm]
n_train = int(0.95 * X.shape[0])
train_ds = TensorDataset(torch.from_numpy(X[:n_train]), torch.from_numpy(y[:n_train]))
test_ds  = TensorDataset(torch.from_numpy(X[n_train:]), torch.from_numpy(y[n_train:]))
train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)
test_loader  = DataLoader(test_ds, batch_size=32)
# 4. Define model (from companion repo)
# from EffecientRes import ConvolutionalAgent
# model = ConvolutionalAgent(action_space=1).cuda()
# 5. Training loop
# optimizer = torch.optim.AdamW(model.parameters(), lr=0.0007)
# criterion = nn.SmoothL1Loss()
# for epoch in range(20):
#     for X_batch, y_batch in train_loader:
#         X_batch, y_batch = X_batch.cuda(), y_batch.cuda()
#         pred = model(X_batch).squeeze()
#         loss = criterion(pred, y_batch.squeeze())
#         optimizer.zero_grad()
#         loss.backward()
#         torch.nn.utils.clip_grad_norm_(model.parameters(), 1.5)
#         optimizer.step()
```

---

## Tips

- **Dataset size:** The full dataset is ~12.2 GB. If you're low on disk space, you can subsample further (e.g. `[::6]` for images and `[::12]` for steering) to halve the working set.
- **Balancing matters:** The raw data is heavily skewed toward near-zero steering (driving straight). Without frequency balancing, the model will learn to just hold the wheel.
- **GPU recommended:** Training is fast on a GPU (~minutes per epoch). CPU training works but is slow.
- **Minimum data:** Aim for at least ~50k raw frames (≈ 27k after sampling) for good results.

---

## Citation

If you use this dataset in your work, please cite the original project:

```bibtex
@misc{watson2025udacity,
  author = {Watson, Jake},
  title = {Self-Driving Car Agent — University of Brighton RO501 Coursework},
  year = {2025},
  url = {https://github.com/Jake1402/University-of-Brighton-RO501-Coursework}
}
```

---

## License

This dataset is provided for educational purposes. The images were generated by the Udacity Self-Driving Car Simulator and do not contain any real-world or personal data.

	---
	license: gpl-3.0
	language:
	- en
	pretty_name: Udacity Car Simulator Test Track
	size_categories:
	- 10K<n<100K
	tags:
	- vision
	- Udacity
	- ComputerVision
	- DeepLearning
	- CNN
	- PyTorch
	---

	# Udacity Car Simulator Dataset

	A preprocessed end-to-end driving dataset collected from the [Udacity Self-Driving Car Simulator](https://github.com/udacity/self-driving-car-sim), intended for training a CNN to predict steering angles from camera input.

	Collected via manual mouse driving in the simulator and processed into NumPy arrays for fast loading during training. This dataset is used by the [University of Brighton RO501 Coursework project](https://github.com/Jake1402/University-of-Brighton-RO501-Coursework).

	---

	## Dataset Structure

	The dataset consists of four `.npy` files:

	\| File \| Shape \| Dtype \| Description \|
	\|------\|-------\|-------\|-------------\|
	\| `center.npy` \| `(N, 3, 160, 80)` \| `float32` \| Centre camera frames \|
	\| `left.npy` \| `(N, 3, 160, 80)` \| `float32` \| Left camera frames \|
	\| `right.npy` \| `(N, 3, 160, 80)` \| `float32` \| Right camera frames \|
	\| `steer.npy` \| `(M,)` \| `float32` \| Smoothed steering angles \|

	Where:
	- N ≈ 27,000 — number of image frames per camera view
	- M ≈ 54,000 — number of steering samples (M = 2 × N)
	- Images are in channel-first format: `(batch, channels, height, width)`
	- Color space is HSV, pixel values normalised to [0, 1]
	- Steering values are in the range [-1, 1] (negative = left, positive = right)

	> Why M = 2 × N? The raw simulator log records one row per frame with all three camera paths. During preprocessing, images are sampled every other row (`[::2]`) to reduce redundancy, while the steering signal is kept at full resolution and smoothed. See [Alignment](#aligning-images-and-steering) below for how to pair them up.

	---

	## Preprocessing Pipeline

	The raw data starts as a `driving_log.csv` produced by the simulator with columns:

	```
	C_img, L_img, R_img, Steer, Accel, Deccel, Velocity
	```

	Each image goes through the following transformations before being saved:

	1. Crop — rows `60:135` are kept (the top of the frame contains no useful driving information)
	2. Resize — to `160 × 80` pixels
	3. Color space — converted from BGR to HSV
	4. Normalise — divided by 255 to give values in `[0, 1]`
	5. Reshape — to channel-first `(3, 160, 80)`

	The steering column is smoothed with a moving average (window = 3) to remove sudden jerks from manual driving.

	---

	## Loading the Dataset

	### Option 1: Download files directly

	Download all four `.npy` files into a local folder (e.g. `npysets/`):

	```bash
	mkdir npysets
	huggingface-cli download JakeWatson/UdacityCarSimDataset --local-dir npysets
	```

	Or use the `datasets` library:

	```python
	from huggingface_hub import snapshot_download

	snapshot_download(
	repo_id="JakeWatson/UdacityCarSimDataset",
	local_dir="npysets",
	allow_patterns=["*.npy"]
	)
	```

	### Option 2: Load with NumPy

	```python
	import numpy as np

	center = np.load("npysets/center.npy") # (N, 3, 160, 80) float32
	left = np.load("npysets/left.npy") # (N, 3, 160, 80) float32
	right = np.load("npysets/right.npy") # (N, 3, 160, 80) float32
	steer = np.load("npysets/steer.npy") # (M,) float32

	print(center.shape) # (27xxx, 3, 160, 80)
	print(steer.shape) # (54xxx,)
	```

	---

	## Aligning Images and Steering

	Because images were sampled at half the rate of steering, you need to subsample both arrays to align them. The companion project uses:

	- Images: every 3rd frame → `arr[::3]`
	- Steering: every 6th value → `arr[::6]`

	This gives matching lengths for each camera view:

	```python
	X_center = np.load("npysets/center.npy")[::3] # (N/3, 3, 160, 80)
	X_left = np.load("npysets/left.npy")[::3] # (N/3, 3, 160, 80)
	X_right = np.load("npysets/right.npy")[::3] # (N/3, 3, 160, 80)
	y = np.load("npysets/steer.npy")[::6] # (N/3,)

	assert X_center.shape[0] == y.shape[0]
	```

	---

	## Building a Training Set

	The standard approach (used in the companion project) concatenates all three camera views and offsets the steering label for left/right views to act as data augmentation:

	```python
	import numpy as np
	import torch
	from torch.utils.data import TensorDataset, DataLoader

	def build_dataset(path="npysets", split=0.95, balance=(31, 550)):
	# Load and align
	X_center = np.load(f"{path}/center.npy")[::3]
	X_left = np.load(f"{path}/left.npy")[::3]
	X_right = np.load(f"{path}/right.npy")[::3]
	y = np.load(f"{path}/steer.npy")[::6]

	# Concatenate views; offset steering for left/right cameras
	X = np.concatenate([X_center, X_left, X_right], axis=0)
	y = np.clip(np.concatenate([y, y + 0.35, y - 0.35]), -1.0, 1.0)

	# Balance by steering frequency (optional but recommended)
	bins, max_per_bin = balance
	bin_edges = np.histogram_bin_edges(y, bins=bins)
	bin_ids = np.digitize(y, bin_edges) - 1
	keep = []
	for b in range(bins):
	idx = np.where(bin_ids == b)[0]
	if len(idx) > max_per_bin:
	idx = np.random.choice(idx, max_per_bin, replace=False)
	keep.append(idx)
	keep = np.concatenate(keep)
	X, y = X[keep], y[keep]

	# Shuffle and split
	perm = np.random.permutation(X.shape[0])
	X, y = X[perm], y[perm]
	split_idx = int(X.shape[0] * split)

	train_ds = TensorDataset(
	torch.from_numpy(X[:split_idx].astype(np.float32)),
	torch.from_numpy(y[:split_idx].astype(np.float32))
	)
	test_ds = TensorDataset(
	torch.from_numpy(X[split_idx:].astype(np.float32)),
	torch.from_numpy(y[split_idx:].astype(np.float32))
	)
	return train_ds, test_ds

	train_ds, test_ds = build_dataset()

	# Create DataLoaders
	train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)
	test_loader = DataLoader(test_ds, batch_size=32, shuffle=False)
	```

	### Why the ±0.35 offset?

	The left and right cameras see the road shifted relative to the centre camera. Adding a constant to their steering labels teaches the model that "if I'm looking from the left camera, I need to steer slightly more right to stay centred." This is a form of view-based data augmentation that improves generalisation.

	---

	## Model Input/Output

	The companion CNN (called UBAC) expects:

	- Input: `(batch_size, 3, 160, 80)` — float32 tensor, HSV, normalised [0, 1]
	- Output: `(batch_size, 1)` — steering angle in `[-1, 1]` (Tanh activation)

	```python
	import torch
	from EffecientRes import ConvolutionalAgent # from the companion repo

	model = ConvolutionalAgent(action_space=1)
	x = torch.randn(4, 3, 160, 80) # batch of 4 images
	steering = model(x) # shape: (4, 1)
	```

	---

	## Quick Start (Full Example)

	```python
	import numpy as np
	import torch
	from torch.utils.data import TensorDataset, DataLoader
	from torch import nn

	# 1. Load data
	center = np.load("npysets/center.npy")[::3]
	left = np.load("npysets/left.npy")[::3]
	right = np.load("npysets/right.npy")[::3]
	steer = np.load("npysets/steer.npy")[::6]

	# 2. Build augmented dataset
	X = np.concatenate([center, left, right], axis=0).astype(np.float32)
	y = np.clip(np.concatenate([steer, steer + 0.35, steer - 0.35]), -1, 1).astype(np.float32)

	# 3. Shuffle & split
	perm = np.random.permutation(X.shape[0])
	X, y = X[perm], y[perm]
	n_train = int(0.95 * X.shape[0])

	train_ds = TensorDataset(torch.from_numpy(X[:n_train]), torch.from_numpy(y[:n_train]))
	test_ds = TensorDataset(torch.from_numpy(X[n_train:]), torch.from_numpy(y[n_train:]))

	train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)
	test_loader = DataLoader(test_ds, batch_size=32)

	# 4. Define model (from companion repo)
	# from EffecientRes import ConvolutionalAgent
	# model = ConvolutionalAgent(action_space=1).cuda()

	# 5. Training loop
	# optimizer = torch.optim.AdamW(model.parameters(), lr=0.0007)
	# criterion = nn.SmoothL1Loss()
	# for epoch in range(20):
	# for X_batch, y_batch in train_loader:
	# X_batch, y_batch = X_batch.cuda(), y_batch.cuda()
	# pred = model(X_batch).squeeze()
	# loss = criterion(pred, y_batch.squeeze())
	# optimizer.zero_grad()
	# loss.backward()
	# torch.nn.utils.clip_grad_norm_(model.parameters(), 1.5)
	# optimizer.step()
	```

	---

	## Tips

	- Dataset size: The full dataset is ~12.2 GB. If you're low on disk space, you can subsample further (e.g. `[::6]` for images and `[::12]` for steering) to halve the working set.
	- Balancing matters: The raw data is heavily skewed toward near-zero steering (driving straight). Without frequency balancing, the model will learn to just hold the wheel.
	- GPU recommended: Training is fast on a GPU (~minutes per epoch). CPU training works but is slow.
	- Minimum data: Aim for at least ~50k raw frames (≈ 27k after sampling) for good results.

	---

	## Citation

	If you use this dataset in your work, please cite the original project:

	```bibtex
	@misc{watson2025udacity,
	author = {Watson, Jake},
	title = {Self-Driving Car Agent — University of Brighton RO501 Coursework},
	year = {2025},
	url = {https://github.com/Jake1402/University-of-Brighton-RO501-Coursework}
	}
	```

	---

	## License

	This dataset is provided for educational purposes. The images were generated by the Udacity Self-Driving Car Simulator and do not contain any real-world or personal data.