Sensor Modality Conversion Using Latent Space for ADAS Testing
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Solution Overview
Problem
Current driving assistance systems and semi-automated vehicle systems require realistic intermediate signals for testing and optimization, but generating these signals from predefined test settings is challenging, especially for different sensor modalities like camera, radar, and LIDAR data.
Innovation Solution
A method using an encoder-decoder arrangement to map measured data from a source modality into a latent space, compressing the data to retain essential information while omitting modality-specific details, and then decoding it into realistic data of a target modality, allowing for efficient conversion and labeling without direct manual labeling of target modality data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measured data from source measurement modality is directly used for testing target measurement modality, then testing accuracy is improved, but manual labeling effort and time increase significantly
Solution Approach 1:
The patent creates synthetic target measurement data by encoding source measurement data into latent space representations and then decoding them into target modality data. This copying approach generates realistic test data without manual labeling, resolving the contradiction between testing accuracy and labeling time investment.
Solution Approach 2:
The encoder-decoder arrangement is trained in advance to learn the mapping between source and target measurement modalities. This preliminary training enables automatic generation of target modality data from source data during testing, eliminating the need for time-consuming manual labeling while maintaining testing accuracy.
2Productivity
If measured data is compressed into latent space with smaller information content, then data processing efficiency is improved, but information loss may occur
Solution Approach 1:
The decoder uses feedback from the compressed latent space representation to reconstruct target measurement data. The training process optimizes the decoder to recover essential information from the compressed form, balancing processing efficiency with information retention through iterative improvement.
Solution Approach 2:
The patent transforms measurement data into a different parameter space (latent space) with optimized dimensionality. This parameter transformation enables efficient processing while the learned encoding preserves critical information needed for accurate target modality reconstruction, resolving the efficiency-information loss trade-off.
3Measurement precision
If encoder-decoder arrangement is trained to compress measured data, then conversion accuracy between modalities is improved, but training time and computational resources increase
Solution Approach 1:
The encoder-decoder arrangement learns a universal mapping that works across multiple measurement modalities. By training on diverse source-target modality pairs, the system achieves accurate conversion for various sensor types (camera, radar, LIDAR) with a single model, improving conversion accuracy while amortizing training time across multiple applications.
Data Source
AI summary
A method for converting measured data of at least one source measurement modality into realistic measured data of at least one target measurement modality. The method includes: the measured data of the source measurement modality are mapped onto representations in a latent space using an encoder of a trained encoder-decoder arrangement, and the representations are mapped onto the realistic measured data of the target measurement modality using the decoder of the encoder-decoder arrangement, the amount of information of the representations of measured data in the latent space being smaller than the amount of information of the measured data.