Autonomous Control Model Refinement Using Fallback Event Learning
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Solution Overview
Problem
Existing autonomous and semi-autonomous control models face challenges in validating and refining their performance under specific scenarios, making it difficult to ensure effective operation in uncertain or contested environments.
Innovation Solution
A simulation-based system with a fallback layer is used to detect failures in trained autonomous control models, identify corresponding events, and refine the models using additional training data analogous to these events, thereby improving their performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training is conducted in an indiscriminate way, then the model can be trained quickly, but it is difficult to validate and refine the model's operation under specific scenarios
Solution Approach 1:
The patent segments the training process into multiple distinct phases: initial indiscriminate training for quick model development, followed by simulation-based validation to identify specific failure scenarios, and then targeted retraining on selected data subsets. This segmentation allows the system to maintain high productivity in the initial phase while ensuring reliability through systematic validation and refinement in subsequent phases.
Solution Approach 2:
The patent performs preliminary validation actions by running the trained model through simulations before deployment to identify specific scenarios where the model fails. This preliminary action reveals weaknesses in the model's performance under particular conditions, enabling targeted refinement rather than indiscriminate retraining, thus improving reliability while maintaining efficiency.
2Reliability
If additional training data is selected and retraining is executed for specific failure events, then the model's performance under particular environments is improved, but the training time and computational resources increase
Solution Approach 1:
The patent applies local quality by selecting and retraining only on specific subsets of training data that correspond to identified failure scenarios, rather than retraining on the entire dataset. This localized approach focuses computational resources on the specific areas where the model needs improvement, enhancing performance under particular environments while minimizing the time and resource investment required.
Solution Approach 2:
The patent employs partial action by performing retraining only on the portions of the data corpus that are analogous to identified failure events, rather than conducting exhaustive retraining on all available data. This partial retraining approach is sufficient to address specific performance deficiencies without the excessive time and computational cost of complete retraining cycles.
3Adaptability or versatility
If simulations are used to validate model performance, then the model can be tested under various virtual driving environments, but the complexity of the validation system increases
Solution Approach 1:
The patent uses simulation environments that create virtual copies of real-world driving scenarios to validate model performance across diverse conditions without requiring physical test vehicles or real-world deployment. This copying approach allows comprehensive validation across multiple environments while keeping the actual validation infrastructure relatively simple, as simulations can be configured and executed on standard computing systems.
Data Source
AI summary
A system for refining a trained autonomous control model is disclosed. The system includes a computing device configured to execute a simulation of a trained autonomous control model for a vehicle model in a simulation environment based on a predefined dataset defining a virtual driving environment and implement a fallback layer configured to detect a failure. In response to the fallback layer detecting the failure of the trained autonomous control model under simulation, the computing device is configured to identify an event in the simulation environment corresponding to the failure of the trained autonomous control model, select additional training data from a data corpus, the additional training data is analogous to the event, and execute a training process to refine the trained autonomous control model using the additional training data such that the trained autonomous control model learns to handle the event with fewer failures.