3D Virtual AI Development for Accurate Training Data at Lower Cost
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Solution Overview
Problem
Conventional AI system development methods rely on physical data and environments, which are time-consuming, costly, and prone to errors, requiring extensive data collection and manual annotation, and are inefficient for adapting to changes in project goals or specifications.
Innovation Solution
Utilizing a real-time 3D virtual environment that simulates physical environments and incorporates objects, sensors, and human-driven avatars to train, validate, and deploy AI systems, enabling data variability and reducing the need for physical setups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical data collection and manual annotation are used for AI training, then data accuracy can be ensured, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of physical environments, objects, and scenarios using 3D modeling and simulation technologies. These virtual replicas replicate real-world conditions accurately while allowing unlimited data generation without additional physical setup costs or time investment, resolving the contradiction between data accuracy and time consumption
Solution Approach 2:
The system pre-generates diverse virtual training data through automated simulation of various scenarios, weather conditions, lighting conditions, and object configurations. This preliminary data preparation eliminates the need for time-consuming manual data collection and annotation campaigns, while maintaining high accuracy through physics-based simulation models
2Reliability
If physical environments and setups are used for AI testing, then real-world validity can be achieved, but cost and complexity increase
Solution Approach 1:
Virtual replicas of real-world environments are created with high fidelity to maintain ecological validity while eliminating the need for complex physical setups. The simulation engine renders photorealistic scenes with accurate physics, lighting, and material properties that preserve real-world interactions without requiring physical construction of test environments
Solution Approach 2:
A single virtual environment platform serves multiple testing purposes simultaneously - it can simulate diverse weather conditions, time of day variations, object configurations, and edge cases that would require multiple separate physical test setups. This universal platform reduces overall complexity while maintaining comprehensive real-world coverage
3Adaptability or versatility
If extensive physical data collection is performed, then data variability for training can be achieved, but resource requirements increase
Solution Approach 1:
The virtual environment dynamically generates infinite variations of training scenarios by programmatically adjusting parameters such as object positions, lighting conditions, weather patterns, camera angles, and scene configurations. This dynamic generation provides unlimited data variability without the resources needed for repeated physical data collection campaigns
Solution Approach 2:
The system pre-configures diverse virtual scenarios and objects in a library, allowing rapid assembly of varied training datasets through parameter combination rather than physical reconstruction. This preliminary preparation of virtual assets enables extensive data variability generation with minimal additional resource investment
4Measurement precision
If manual annotation processes are used, then data quality can be maintained, but productivity decreases
Solution Approach 1:
Virtual objects and environments provide automatically generated ground truth data through their digital representations. Annotation information such as object boundaries, labels, and attributes are inherently available from the 3D model data and simulation parameters, eliminating manual annotation entirely while maintaining high data quality through programmatically precise definitions
Data Source
AI summary
Systems and methods are provided to create training data, validate, deploy and test artificial intelligence (AI) systems in a virtual development environment, incorporating virtual spaces, objects, machinery, devices, subsystems, and actual human action and behavior.