Synthetic Radar Data Generation via Latent Space Decoding
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Solution Overview
Problem
The scarcity of labeled training data for machine learning models trained on radar, LIDAR, and ultrasound measurements poses a significant challenge due to the difficulty in interpreting raw data from these modalities, making it time-consuming and expensive to generate accurate ground truth for object recognition and trajectory adjustments in automated vehicle systems.
Innovation Solution
A method is developed to generate synthetic measurement data using a combination of encoder-decoder tandems and prior-transforms, allowing for the creation of compressed representations that are indistinguishable from actual data, thereby reducing the complexity of data generation and enabling efficient training of machine learning models with significantly less human effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If synthetic measurement data is generated using traditional methods, then training data availability increases, but data quality and indistinguishability from actual measurement data deteriorates
Solution Approach 1:
The patent uses a decoder trained in tandem with an encoder to copy the statistical properties and structural characteristics of actual measurement data. The decoder transforms compressed representations into synthetic radar data that statistically resembles real data, achieving high fidelity copies that are indistinguishable from actual measurements while enabling unlimited data generation.
Solution Approach 2:
The tandem training of encoder and decoder creates a feedback loop where the decoder's output is compared against actual measurement data, and gradients are backpropagated to refine the decoder's parameters. This feedback mechanism ensures the synthetic data continuously improves in quality and indistinguishability from real data during the training process.
2Measurement precision
If measurement data is used directly for training, then training accuracy is maintained, but labeling cost and time consumption increases
Solution Approach 1:
The patent creates synthetic copies of measurement data with automatic ground truth labels by transforming compressed representations through the trained decoder. These synthetic data copies retain the statistical properties and labeling information of the source data, providing unlimited labeled training data without human annotation effort.
Solution Approach 2:
The encoder-decoder tandem is pre-trained on actual measurement data with ground truth labels before synthetic data generation. This preliminary training establishes the decoder's capability to produce high-quality synthetic data with accurate labels, enabling subsequent rapid generation of labeled training data without time-consuming human annotation.
3Productivity
If compressed representations are used, then data processing efficiency increases, but information loss may occur
Solution Approach 1:
The patent transforms measurement data into compressed representations in a latent space, changing the parameterization of the data. The decoder then transforms these compressed parameters back into synthetic measurement data, preserving essential information while enabling efficient processing. The tandem training ensures the parameter transformation maintains data fidelity.
Solution Approach 2:
The compressed representation in latent space acts as an intermediary between the encoder and decoder. This intermediate form enables efficient data manipulation and generation while the trained decoder ensures accurate reconstruction of synthetic measurement data, balancing compression efficiency with information preservation.
Data Source
AI summary
A method for generating synthetic measurement data indistinguishable from actual measurement data captured by a first physical measurement modality. The first physical measurement modality is based on emitting an interrogating wave towards an object and recording a reflected wave coming from the object in a manner that allows for a determination of the time-of-flight between the emission of the interrogating beam and the arrival of the reflected wave. The method includes: obtaining a first compressed representation of the synthetic measurement data in a first latent space, wherein this first latent space is associated with a first decoder that is trained to map each element of the first latent space to a record of synthetic measurement data that is indistinguishable from records of actual measurement data of the first physical measurement modality, and applying the first decoder to the first compressed representation, so as to obtain the sought synthetic measurement data.