Simulated Data Tuning for Neural Network Activation Matching
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Solution Overview
Problem
Simulated environments for training and testing autonomous vehicle AI systems often lack realism, leading to inefficient resource usage and suboptimal neural network activation, as they require generating photorealistic scenarios that consume excessive computational resources.
Innovation Solution
A method to tune simulated data by training a data model using real-world neural network activations, allowing for the adjustment of parameters to create simulated environments that activate neural networks in a manner similar to real environments, thereby reducing the need for photorealism and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If photorealistic simulated environments are generated to improve realism, then the realism of simulated environments is improved, but computational resources are excessively consumed
Solution Approach 1:
The patent changes the parameters of simulated environments from photorealistic to non-photorealistic while maintaining neural network activation similarity. This involves adjusting visual parameters such as texture detail, lighting complexity, and geometric fidelity to reduce computational load while preserving the essential features needed for effective AI training
Solution Approach 2:
Instead of creating photorealistic copies of real environments, the patent creates simplified representations that copy only the essential structural and semantic features needed for neural network activation. This allows the simulated environments to trigger similar neural responses without replicating all visual details
2Productivity
If simulated environments are simplified to reduce computational resources, then resource efficiency is improved, but the realism and effectiveness of neural network activation deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where neural network activations from simulated environments are compared against activations from real environments. This feedback loop allows iterative adjustment of simulated environment parameters to ensure they produce similar neural responses, thereby maintaining training effectiveness while using simplified representations
Solution Approach 2:
The patent performs preliminary training or validation using a subset of simplified simulated environments to identify which features are most critical for neural network activation. This preliminary action allows optimization of the simulation parameters before full-scale training, ensuring resource efficiency is maintained without sacrificing activation similarity
Data Source
AI summary
Techniques described herein are directed to comparing, using a machine-trained model, neural network activations associated with data representing a simulated environment and activations associated with data representing real environment to determine whether the simulated environment is causes similar responses by the neural network, e.g., a detector. If the simulated environment and the real environment do not activate the same way (e.g., the variation between neural network activations of real and simulated data meets or exceeds a threshold), techniques described herein are directed to modifying parameters of the simulated environment to generate a modified simulated environment that more closely resembles the real environment.