Radar Data Tensor Conversion for ML Training Adaptation
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Solution Overview
Problem
Current methods for automated driving require extensive manual labeling of radar data for training machine learning models, making it expensive and inefficient, especially when radar sensor configurations change, rendering previously collected data unusable.
Innovation Solution
A method to convert source radar data into target radar data using a source tensor and machine learning models, allowing existing labeled data to be reused across different radar system configurations, including changes in sensor position or manufacturer, by generating realistic synthetic data that accounts for uncertainties in radar radiation observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of radar data is performed to train machine learning models, then object recognition accuracy is improved, but training cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary action by pre-processing radar data into tensor representations and pre-training machine learning models with synthetic radar data before actual deployment. This allows the model to be ready for object recognition tasks without requiring extensive manual labeling and training at the time of deployment, thus reducing training time consumption while maintaining recognition accuracy.
2Adaptability or versatility
If radar sensor configuration is changed to improve performance or adapt to new requirements, then system capability is improved, but previously collected training data becomes unusable
Solution Approach 1:
The system uses copying by generating synthetic radar data that mimics the characteristics of real radar data for different sensor configurations. Instead of requiring actual physical re-recording of training data for each new configuration, the system creates synthetic copies that preserve the statistical properties and patterns needed for training, thus maintaining training data usability across configuration changes.
Solution Approach 2:
The system applies parameter changes by transforming radar data tensors according to different sensor configuration parameters such as position, orientation, and frequency characteristics. This allows the same base training data to be adapted to multiple configurations through parameter transformation rather than requiring separate datasets for each configuration, preserving training data usability.
3Measurement precision
If physical re-recording of radar data is performed for new sensor configurations, then data accuracy for new configuration is improved, but effort and cost increase drastically
Solution Approach 1:
The system creates synthetic copies of radar data that accurately represent new sensor configurations without requiring physical re-recording. These synthetic copies are generated by transforming existing radar data tensors to match the statistical and spatial characteristics of the new configuration, providing accurate training data with minimal effort.
Solution Approach 2:
The system replaces the mechanical process of physical re-recording with a computational approach. Instead of physically moving sensors and re-collecting data in the field, the system uses tensor transformations and machine learning to synthetically generate data for new configurations, dramatically reducing the effort and cost while maintaining data accuracy.
4Reliability
If extensive manual labeling is performed to account for uncertainties in radar observations, then model robustness is improved, but processing cost and complexity increase
Solution Approach 1:
The system replaces manual labeling processes with automated tensor-based processing and machine learning. Uncertainties in radar observations are handled through probabilistic tensor representations and learned uncertainty models rather than requiring extensive manual annotation of uncertain cases, reducing processing complexity while maintaining model robustness.
Data Source
AI summary
A method is disclosed for converting source radar data of a source configuration of a radar system target radar data of a target configuration. The method comprises: providing a source array of grid cells for source reflex locations; determining, for each respective grid cell in the source array, a probability or frequency that source reflex locations are located in the respective grid cell; forming a source tensor including the source array populated with the probability or frequency for each grid cell; transforming the source tensor into a target tensor including a target array of grid cells for the target reflex locations and indicating the probabilities or frequencies of the target reflex locations for each respective grid cell; and generating the target radar data by sampling the location coordinates of the target reflex locations.