Sensor Data Transformation for Multi-Configuration Model Training
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Solution Overview
Problem
Machine learning models trained using data from one sensor configuration may not produce accurate outputs when input with data from a different sensor configuration, due to differences in sensor mounting and data perspectives across various vehicles, leading to inconsistent performance across different vehicle types.
Innovation Solution
A system and method that transform sensor data from one configuration to another using perspective transformations and machine learning techniques, separating objects from backgrounds, and recombining them to train models for different sensor configurations, incorporating computer graphics and machine learning methods for 3D scene transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is collected from a specific sensor configuration for model training, then the model achieves accurate recognition for that configuration, but the model fails to produce accurate outputs when applied to different sensor configurations
Solution Approach 1:
The patent creates virtual copies of sensor data by transforming data from one sensor configuration into synthetic data representing different sensor configurations. This allows the model to be trained on multiple virtual sensor configurations without requiring physical sensors for each configuration, thereby improving adaptability while maintaining accuracy through realistic data synthesis
Solution Approach 2:
The patent transforms sensor data by changing parameters such as sensor positions, orientations, and mounting configurations. By systematically varying these parameters to generate diverse training data, the model learns to recognize patterns across different sensor configurations, resolving the contradiction between configuration-specific accuracy and broad adaptability
2Reliability
If sensor configurations vary across different vehicles, then each vehicle type requires separate model training, but this increases training time and computational resources
Solution Approach 1:
The patent performs preliminary transformations of sensor data to pre-compute virtual sensor configurations and perspectives before actual model training. By preparing diverse configuration data in advance through transformation operations, the system eliminates the need for separate training processes for each vehicle type, thereby maintaining reliability across vehicle types while significantly reducing total training time
Solution Approach 2:
The patent creates a universal training framework where a single model can be trained on transformed data representing multiple sensor configurations simultaneously. This multi-functional approach allows one model to serve multiple vehicle types and sensor configurations, improving performance consistency while reducing the time and resources required compared to training separate models for each configuration
3Reliability
If sensor mounting positions and orientations differ across vehicles, then the same model produces inconsistent outputs, but retraining models for each configuration reduces productivity
Solution Approach 1:
The patent generates virtual copies of sensor data with transformed mounting positions and orientations through mathematical transformations. This allows the model to learn from diverse mounting configurations without requiring physical reconfiguration or separate training processes, thereby maintaining output consistency while preserving deployment productivity
Solution Approach 2:
The patent systematically transforms sensor data by changing mounting position and orientation parameters to create diverse training examples. By incorporating these parameter variations during training, the model learns to produce consistent outputs across different mounting configurations, eliminating the need for retraining and maintaining high deployment efficiency
Data Source
AI summary
A system includes a processor and a memory storing instructions which when executed by the processor configure the processor to receive first data from a first set of sensors arranged in a first configuration. The instructions configure the processor to transform the first data to a second data to train a model to recognize third data captured by a second set of sensors arranged in a second configuration. The second configuration is different than the first configuration. The instructions configure the processor to train the model based on the second set of sensors sensing the second data to recognize the third data captured by the second set of sensors arranged in the second configuration.