3D Virtual AI Training Environment for Authentic Sensor Data
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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 manual annotation and reconfiguration for changes in project goals, and are sensitive to environmental factors.
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 efficient testing without physical reconfiguration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical data and real environments are used for AI training and testing, then the AI system can learn from authentic scenarios, but the process becomes time-consuming, costly, and requires extensive manual annotation and physical reconfiguration for changes
Solution Approach 1:
The patent creates virtual copies of physical environments, objects, and scenarios using 3D modeling and simulation technologies. These virtual replicas preserve the authentic characteristics needed for AI training while eliminating the need for physical data collection and manual annotation, allowing rapid generation of diverse training data without time-consuming fieldwork
Solution Approach 2:
The system pre-generates diverse virtual training scenarios, objects, and environmental conditions before AI training begins. By preparing comprehensive virtual datasets in advance including varied lighting, weather, object positions, and sensor readings, the system eliminates the need for time-consuming data collection during the training process itself
2Reliability
If physical environments and equipment are used for AI testing, then real-world performance can be validated, but the system becomes sensitive to environmental factors and requires extensive physical setup and reconfiguration
Solution Approach 1:
The virtual testing environment dynamically adjusts scenarios, environmental conditions, and test parameters without physical reconfiguration. Test cases can be modified, added, or removed through software updates, allowing the system to adapt to changing project requirements while maintaining consistent validation standards across different scenarios
Solution Approach 2:
The virtual environment serves multiple testing functions simultaneously - it can validate object detection, tracking, classification, and response behaviors across diverse scenarios including different lighting conditions, weather, object types, and environmental layouts, all within a single unified testing platform
3Adaptability or versatility
If diverse training data is collected from physical sources, then the AI system can handle varied situations, but the cost and complexity of data collection and annotation increases significantly
Solution Approach 1:
The system uses virtual copies to generate diverse training data across multiple dimensions including object types, positions, orientations, lighting conditions, weather scenarios, and sensor readings. This virtual replication approach creates comprehensive data variability without requiring complex physical data collection infrastructure or manual annotation processes
Solution Approach 2:
The virtual environment automatically generates and annotates training data through programmed scenarios and simulated sensor responses. Objects, environmental conditions, and sensor readings are automatically created and labeled by the simulation system itself, eliminating the need for manual data collection teams and annotation processes
4Reliability
If physical sensors and cameras are deployed in real environments, then authentic sensor data can be obtained, but the setup cost, time, and environmental sensitivity increase
Solution Approach 1:
The system creates virtual sensor copies that replicate the response characteristics of physical sensors including cameras, LIDAR, and other detection devices. These virtual sensors generate authentic-looking sensor data within the simulation environment, preserving the statistical and spatial properties of real sensor outputs without requiring physical hardware deployment
Solution Approach 2:
The virtual environment acts as an intermediary between AI algorithms and physical sensor data characteristics. It translates physical sensor response patterns into virtual sensor readings that maintain the same statistical properties and spatial relationships, allowing AI systems to learn sensor data patterns without direct physical sensor deployment
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.