Are you a researcher or developer diving into the world of autonomous driving (AD) or advanced driver-assistance systems (ADAS)? Finding high-quality autonomous driving datasets can be a major hurdle. Luckily, a wealth of free datasets are available to train your computer vision models. This comprehensive guide explores the best open-source resources, covering both real-world and synthetic data, ensuring you have the tools to succeed.
Real-World Automotive Datasets: Essential for AD/ADAS Training
KITTI Vision Benchmark Suite:
A classic in AD research, KITTI offers stereo images, LiDAR point clouds, and GPS/IMU data. Perfect for object detection, tracking, and semantic segmentation.
nuScenes:
From Motional, nuScenes delivers data from six cameras, five radars, and one LiDAR, with detailed annotations for 3D object detection and scene understanding.
Cityscapes:
Focus on urban environments with Cityscapes, ideal for semantic segmentation and pixel-level scene understanding.
CityScapes data can be augmented by Repli5 AI to generate targeted variations to broaden coverage and stimulate model learning.
BDD100K:
A large diverse driving video dataset.
MAN TruckScenes Dataset:
Specifically designed for heavy vehicle AD development, providing realistic highway driving data.
Link to MAN TruckScenes Dataset
Zenseact Open Dataset (ZOD):
A large-scale dataset with diverse driving conditions and high-quality annotations.
Link to Zenseact Open Dataset (ZOD)
Lyft Level 5 Dataset:
Created for prediction tasks, this large-scale dataset is a valuable resource.
Waymo Open Dataset:
A comprehensive dataset with a wide variety of sensor data.
A2D2 Dataset:
Audi's dataset provides images, 3D point clouds, and detailed annotations.
Mapillary Vistas Dataset:
A rich street-level imagery dataset.
Link to Mapillary Vistas Dataset
ApolloScape Dataset:
A large dataset from China, featuring diverse driving conditions.
Comma2k Dataset:
Over 33 hours of highway driving data from California.
Oxford RobotCar Dataset:
A unique dataset with repeated routes through Oxford, captured over a year.
Synthetic Automotive Datasets: The Future of Training
CARLA Simulator Datasets:
Generate customizable synthetic data with CARLA, perfect for creating diverse scenarios and RGB training data.
Repli5 Open Dataset (Coming 2025):
Anticipate the release of Repli5's open synthetic dataset in 2025, featuring AI-generated variations for enhanced realism.
Repli5 Generative AI Datasets:
For custom synthetic datasets, augmented with AI variations, Repli5 offers cutting-edge solutions. Visit Repli5.com to explore our offerings.
Carla 4 (Unreal Engine 4) low fidelity datasets are accessible, but lack realism. 
RealSim, Repli5 AI augmented data improves fidelity to stimulate model learning.
Why Use Open-Source Datasets?
Cost-Effective: Access high-quality data without breaking the bank.
Community Driven: Benefit from continuous improvements and support.
Reproducible Research: Ensure your work is built on solid, verifiable data.
Choosing the Right Dataset for Your Project:
Consider these factors:
Your specific research or development goals.
The required sensor data (camera, LiDAR, radar).
Environmental conditions and driving scenarios.
Real world, or synthetic dataset requirements.
If your project requires AI augmented data.
By utilizing these free automotive datasets and the innovative synthetic solutions from Repli5, you can accelerate your computer vision projects and contribute to the future of autonomous driving.
If you're looking for something specific, such as a unique geography, weather condition or type of vehicle - reach out to us at Repli5 and we'll provide you with a free quotation to generate the training dataset tailored to your specific needs.
Comments