Machine-Learned Model Training for Sensor Parameter Optimization
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Solution Overview
Problem
Autonomous vehicles face suboptimal sensor data collection due to manually set parameter settings that may not accurately represent the environment for improved vehicle performance, leading to degraded object detection and navigation.
Innovation Solution
A machine-learned model is trained to determine and dynamically adjust sensor parameter settings based on performance metrics, optimizing sensor settings in real-time to improve perception system accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter settings are used to accurately represent the environment based on human perceptibility, then human perceptibility is improved, but vehicle performance is degraded
Solution Approach 1:
The patent changes the optimization criterion from human perceptibility to vehicle performance metrics. The machine learning model learns optimal sensor parameters by minimizing prediction errors in vehicle performance outcomes, thereby changing the parameter optimization goal from human-centric to vehicle-performance-centric.
Solution Approach 2:
The patent replaces manual engineer tuning with an automated machine learning system. The ML model automatically determines optimal sensor parameters based on performance metrics, substituting the mechanical/manual adjustment process with an intelligent automated system that optimizes for vehicle performance rather than human perceptibility.
2Reliability
If fixed parameter settings are used for one set of circumstances, then those specific circumstances are optimized, but other circumstances suffer from suboptimal data collection
Solution Approach 1:
The patent makes sensor parameters dynamic rather than fixed. The machine learning model continuously determines optimal parameters based on current environmental conditions and vehicle state, allowing the system to adapt to different circumstances automatically. This dynamic adjustment ensures optimal performance across varying conditions without requiring manual reconfiguration.
Solution Approach 2:
The system performs self-optimization by automatically adjusting sensor parameters based on real-time performance feedback. The machine learning model monitors vehicle performance metrics and autonomously determines the optimal sensor settings without external intervention, enabling the system to serve itself across different operational circumstances.
3Ease of operation
If manual parameter adjustment is used, then engineer control is maintained, but optimization efficiency and real-time adaptation are reduced
Solution Approach 1:
The system implements self-service by enabling the vehicle to automatically optimize its own sensor parameters through machine learning. The ML model continuously learns from performance data and autonomously adjusts parameters without requiring ongoing engineer intervention, thereby maintaining ease of operation while dramatically improving optimization efficiency and enabling real-time adaptation.
Solution Approach 2:
The patent introduces a feedback loop where vehicle performance metrics are continuously monitored and fed back to the machine learning model. This feedback mechanism enables automatic parameter optimization based on actual performance outcomes, improving efficiency while maintaining the ability for engineers to oversee and adjust the optimization process when needed.
Data Source
AI summary
This disclosure describes methods, apparatuses, and systems for training machine-learned models to determine optimal parameter settings associated with a sensor. For example, a system can input training data into a first machine-learned model configured to output an optimized sensor setting associated with a sensor parameter, the training data includes first sensor data and second sensor data. The system can input the training data into a second machine-learned model configured to output a detected feature. The system can determine a difference between the detected feature and a known feature. The system can alter a model configuration parameter used to capture or process the training data to minimize the difference to obtain a trained first or second machine-learned model. The system can further transmit the trained first and second machine-learned models to a vehicle configured to be controlled based on the first and the second machine-learned models.