Adaptive Simulator Parameter Optimization for Danger Prediction
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Solution Overview
Problem
Existing machine learning models struggle to effectively utilize simulated data for tasks like danger prediction in scenarios where real data is scarce or hard to obtain, such as traffic accidents, due to the fixed and unknown data distribution assumption in prior works.
Innovation Solution
The approach involves generating fully-annotated simulated training data using a simulator with adjustable parameters, where reinforcement learning is used to optimize these parameters based on the accuracy of the machine learning model, allowing for adaptive data generation that improves model performance on specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If simulated data is used for training machine learning models, then data availability is improved, but data distribution accuracy deteriorates
Solution Approach 1:
The patent applies dynamics by making the simulator parameters adjustable and optimizable rather than fixed. The system dynamically adjusts simulator parameters based on performance feedback to generate simulated data that better matches real data distributions, resolving the contradiction between data availability and distribution accuracy.
Solution Approach 2:
The patent changes parameters of the simulator to optimize the quality of generated data. By adjusting simulator parameters and using reinforcement learning to find optimal parameter settings, the system improves data distribution accuracy while maintaining the advantage of abundant simulated data generation.
2Reliability
If reinforcement learning is used to optimize simulator parameters, then model performance is improved, but computational complexity increases
Solution Approach 1:
The patent implements feedback by using the performance of the machine learning model on real data as a signal to guide the optimization of simulator parameters through reinforcement learning. This feedback loop allows the system to iteratively improve model performance while managing computational complexity through efficient parameter optimization.
3Manufacturing precision
If fully-annotated simulated training data is generated, then training data quality is improved, but data generation time increases
Solution Approach 1:
The patent applies preliminary action by generating and storing fully-annotated simulated training data in advance before it is needed for model training. The system pre-generates comprehensive training datasets with all necessary annotations, reducing the need for repeated data generation and saving time during the actual training process.
Data Source
AI summary
A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy. The method further includes predicting a dangerous condition relative to a motor vehicle and providing a warning to an entity regarding the dangerous condition by applying the trained machine learning model to actual unlabeled data for the vehicle.