Autonomous Vehicle ML Model Selection via Bayesian Optimization
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Solution Overview
Problem
Autonomous vehicles face challenges in continuously generating and selecting optimal machine learning models for operation, as different training data sets result in varying model effectiveness and efficiency, leading to suboptimal decision-making in real-world scenarios.
Innovation Solution
A system that utilizes sensor data and multi-objective Bayesian optimization to identify and generate optimal trained machine learning models by evaluating candidate deep learning architectures, selecting Pareto-optimal models, and re-training them for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If different training data sets are used to generate machine learning models, then model effectiveness varies, but model selection complexity increases
Solution Approach 1:
The system employs multi-objective Bayesian optimization that automatically evaluates and selects optimal machine learning models based on multiple performance metrics without requiring manual intervention. The optimization framework self-manages the complex process of comparing different training data sets and their corresponding models, identifying Pareto-optimal solutions autonomously.
Solution Approach 2:
The patent changes the evaluation parameters from single-metric assessment to multi-metric assessment including accuracy, computational efficiency, and resource utilization. By optimizing across multiple parameters simultaneously, the system identifies models that balance effectiveness and efficiency, resolving the contradiction between model performance and selection complexity.
2Measurement precision
If more training data is collected continuously, then model accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system implements continuous model optimization where trained models are continuously evaluated against new training data using multi-objective Bayesian optimization. This continuous process allows the system to incrementally improve model accuracy by incorporating new data while maintaining operational efficiency through automated selection of optimal models at each iteration.
Solution Approach 2:
The patent applies partial re-training strategies where only selected models undergo full re-training while others are evaluated through faster assessment methods. This selective approach balances the need for improved accuracy with computational constraints, avoiding excessive processing time while still achieving model improvement through targeted optimization.
3Reliability
If multiple machine learning models are evaluated, then optimal model selection improves, but evaluation complexity increases
Solution Approach 1:
The multi-objective Bayesian optimization framework implements feedback loops where model performance across multiple metrics is continuously measured and fed back into the optimization process. This feedback mechanism systematically evaluates multiple models and uses the results to guide subsequent evaluation efforts, improving optimal model selection while managing complexity through structured feedback-driven refinement.
Data Source
AI summary
Machine learning model optimization systems and methods are disclosed. A system receives sensor data captured by one or more sensors of a vehicle during a first time period. The vehicle uses a first trained machine learning (ML) model for one or more decisions of a first decision type during the first time period. The system generates a second trained ML model at least in part by using the sensor data to train the second trained ML model. The system identifies an optimal trained ML model from a plurality of trained ML models. The plurality of trained ML models includes the first trained ML model and the second trained ML model. The system causes the vehicle to use the optimal trained ML model for one or more further decisions of the first decision type during a second time period after the first time period.