📊

Dataset

Autonomous Driving Carla

Name: Autonomous Driving Carla
Creator: jkdxbns
License: MIT

by jkdxbns hf-dataset--jkdxbns--autonomous-driving-carla

Nexus Index

31.8 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 51

R: Recency 54

Q: Quality 30

Tech Context

Vital Performance

0 DL / 30D

0.0%

Source →

Data Integrity 31.8 FNI Score

- Size

- Rows

Parquet Format

- Tokens

Dataset Information Summary
Entity Passport
Registry ID	hf-dataset--jkdxbns--autonomous-driving-carla
License	MIT
Provider	huggingface

📜

Cite this dataset

Academic & Research Attribution

BibTeX

@misc{hf_dataset__jkdxbns__autonomous_driving_carla,
  author = {jkdxbns},
  title = {Autonomous Driving Carla Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/jkdxbns/autonomous-driving-carla}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

jkdxbns. (2026). Autonomous Driving Carla [Dataset]. Free2AITools. https://huggingface.co/datasets/jkdxbns/autonomous-driving-carla

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Nexus Index V2.0

Methodology Index Protocol

31.8

TOP 100% SYSTEM IMPACT

Semantic (S) 50

Authority (A) 0

Popularity (P) 51

Recency (R) 54

Quality (Q) 30

💬 Index Insight

FNI V2.0 for Autonomous Driving Carla: Semantic (S:50), Authority (A:0), Popularity (P:51), Recency (R:54), Quality (Q:30).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit

Unbiased Data Node Refresh: VFS Live

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Downloads

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Schema structure is shown in the Field Logic panel when available.

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Dataset Specification

CARLA Autonomous Driving Dataset

Custom datasets for autonomous driving in CARLA simulator

Created for CMPE 789 - Robot Perception at Rochester Institute of Technology

📊 Dataset Overview

This repository contains two custom-generated datasets from the CARLA 0.9.15 simulator for training autonomous driving perception models:

Dataset	Task	Images	Format	Size
YOLO Dataset	Object Detection	4,000	YOLOv8/v11	~1.2 GB
UFLD Dataset	Lane Detection	10,000	TuSimple-like	~3.4 GB

🚗 YOLO Object Detection Dataset

Description

Custom object detection dataset generated from CARLA Town01 with optimized graphics settings. Contains annotations for vehicles, pedestrians, traffic lights, and speed limit signs.

Classes

Class ID	Class Name	Total Annotations	Description
0	`vehicle`	2,797	Cars, trucks, vans
1	`pedestrian`	3,329	Walking pedestrians
2	`traffic_light`	409	Traffic signals
3	`speed_limit`	43	Speed limit signs

Total Annotations: 6,578

Split Distribution

text

┌─────────────────────────────────────────────────────────────────┐
│                    YOLO Dataset Splits                          │
├─────────────────────────────────────────────────────────────────┤
│  Train (70.3%)  ████████████████████████████░░░░░░░░  2,812     │
│  Val (19.8%)    ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░    790     │
│  Test (9.9%)    ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    398     │
└─────────────────────────────────────────────────────────────────┘

Split	Images	Vehicle	Pedestrian	Traffic Light	Speed Limit
Train	2,812	1,949	2,252	301	33
Val	790	562	712	77	5
Test	398	288	365	31	5
Total	4,000	2,797	3,329	409	43

Class Distribution Chart

text

Class Distribution (Total Annotations)
═══════════════════════════════════════════════════════════════════

pedestrian    ████████████████████████████████████████████  3,329 (50.6%)
vehicle       ███████████████████████████████████░░░░░░░░░  2,797 (42.5%)
traffic_light █████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    409 (6.2%)
speed_limit   █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░     43 (0.7%)

═══════════════════════════════════════════════════════════════════

Format

Standard YOLO format with normalized bounding boxes:

text

Example:

text

0 0.499978 0.660904 0.117395 0.253719
1 0.726564 0.544706 0.078319 0.045380
2 0.545117 0.490625 0.004297 0.028472

Directory Structure

text

yolo_dataset/
├── train/
│   ├── images/          # 2,812 JPG images (1640×590)
│   └── labels/          # 2,812 TXT label files
├── val/
│   ├── images/          # 790 JPG images
│   └── labels/          # 790 TXT label files
├── test/
│   ├── images/          # 398 JPG images
│   └── labels/          # 398 TXT label files
├── dataset.yaml         # YOLO configuration file
└── classes.json         # Class ID mapping

🛣️ UFLD Lane Detection Dataset

Description

Lane detection dataset in TuSimple-like format, generated from CARLA Town01 for training Ultra-Fast Lane Detection (UFLD) models. Contains polyline annotations for left and right lane boundaries.

Split Distribution

text

┌─────────────────────────────────────────────────────────────────┐
│                    UFLD Dataset Splits                          │
├─────────────────────────────────────────────────────────────────┤
│  Train (70%)    ████████████████████████████░░░░░░░░  7,000     │
│  Val (20%)      ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░  2,000     │
│  Test (10%)     ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  1,000     │
└─────────────────────────────────────────────────────────────────┘

Split	Images	Percentage
Train	7,000	70%
Val	2,000	20%
Test	1,000	10%
Total	10,000	100%

Annotation Format

Each image has:

Polyline annotations (.lines.txt): X,Y coordinate pairs for lane boundaries
Segmentation labels (.png): Pixel-wise lane masks
Ground truth list (train_gt.txt, val_gt.txt): Image paths with lane existence flags

Ground Truth Format:

text

Example:

text

/images/train/000000.jpg /labels/train/000000.png 1 1 0 0

(Two lanes detected: left and right)

Directory Structure

text

ufld_dataset/
├── annotations/
│   ├── train/           # 7,000 .lines.txt files
│   ├── val/             # 2,000 .lines.txt files
│   └── test/            # 1,000 .lines.txt files
├── images_train.zip     # 7,000 JPG images (2.3 GB)
├── images_val.zip       # 2,000 JPG images (647 MB)
├── images_test.zip      # 1,000 JPG images (325 MB)
├���─ labels_train.zip     # 7,000 PNG segmentation masks (18 MB)
├── labels_val.zip       # 2,000 PNG segmentation masks (5.1 MB)
├── labels_test.zip      # 1,000 PNG segmentation masks (2.6 MB)
└── list/
    ├── train_gt.txt     # Training split ground truth
    ├── val_gt.txt       # Validation split ground truth
    └── test.txt         # Test image list

Note: Images and labels are provided as ZIP files to reduce file count. Extract after downloading.

📥 Download Instructions

Using Hugging Face CLI

bash

# Install huggingface_hub if needed
pip install huggingface_hub

# Download YOLO dataset (ready to use)
huggingface-cli download jkdxbns/autonomous-driving-carla yolo_dataset --repo-type dataset --local-dir ./

# Download UFLD dataset
huggingface-cli download jkdxbns/autonomous-driving-carla ufld_dataset --repo-type dataset --local-dir ./

Extract UFLD ZIP Files

bash

cd ufld_dataset

# Extract images
unzip images_train.zip -d .
unzip images_val.zip -d .
unzip images_test.zip -d .

# Extract labels
unzip labels_train.zip -d .
unzip labels_val.zip -d .
unzip labels_test.zip -d .

Using Python

python

from huggingface_hub import snapshot_download

# Download entire dataset
snapshot_download(
    repo_id="jkdxbns/autonomous-driving-carla",
    repo_type="dataset",
    local_dir="./datasets"
)

🖼️ Image Specifications

Property	Value
Resolution	1640 × 590 pixels
Format	JPEG
Color	RGB
FOV	150° (wide-angle)
Camera Position	Front-mounted, 2.4m height
Simulator	CARLA 0.9.15
Map	Town01 (optimized graphics)

🎯 Intended Use

These datasets are designed for:

Training object detection models (YOLO, Faster R-CNN, etc.) for autonomous driving
Training lane detection models (UFLD, LaneNet, etc.)
Research in simulation-to-real transfer learning
Educational purposes in robotics and computer vision courses
Benchmarking perception algorithms in controlled environments

📈 Training Results

Models trained on these datasets achieve:

Model	Task	Performance
YOLO11n	Object Detection	mAP50: 0.85+
UFLD ResNet-18	Lane Detection	Accuracy: 92%+

Pre-trained weights available at: 🤗 jkdxbns/autonomous-driving-carla

📚 Citation

If you use this dataset in your research, please cite:

bibtex

@misc{carla_autonomous_driving_dataset_2024,
  author = {Jatin Khokhani},
  title = {CARLA Autonomous Driving Dataset},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/jkdxbns/autonomous-driving-carla}
}

📄 License

This dataset is released under the MIT License.

GitHub Repository: autonomous-driving-carla
Pre-trained Models: Hugging Face Models
CARLA Simulator: carla.org
UFLD Paper: Ultra Fast Structure-aware Deep Lane Detection

Created with ❤️ for CMPE 789 - Robot Perception @ RIT

📊 Structured Schema (Zero-Fabrication)

Feature Key	Data Type
`text`	`string`

Estimated Rows: 28,455

Social Proof

HuggingFace Hub

28.3KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Dataset Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: hf-dataset--jkdxbns--autonomous-driving-carla
slug: jkdxbns--autonomous-driving-carla
source: huggingface
author: jkdxbns
license: MIT
tags: task_categories:object-detection, task_categories:image-segmentation, language:en, license:mit, size_categories:10k<n<100k, format:text, modality:image, modality:text, library:datasets, library:mlcroissant, arxiv:2004.11757, region:us, autonomous-driving, carla, yolo, lane-detection, ufld, simulation, robotics, computer-vision

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 28,282
stars: 0
forks: 0

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🧬 Field Logic

Dataset Specification

CARLA Autonomous Driving Dataset

📊 Dataset Overview

🚗 YOLO Object Detection Dataset

Description

Classes

Split Distribution

Class Distribution Chart

Format

Directory Structure

🛣️ UFLD Lane Detection Dataset

Description

Split Distribution

Annotation Format

Directory Structure

📥 Download Instructions

Using Hugging Face CLI

Extract UFLD ZIP Files

Using Python

🖼️ Image Specifications

🎯 Intended Use

📈 Training Results

📚 Citation

📄 License

🔗 Related Resources

📊 Structured Schema (Zero-Fabrication)

Social Proof

🛡️ Dataset Transparency Report

🆔 Identity & Source

⚙️ Technical Specs

📊 Engagement & Metrics