Conditional GAN Data Translation for Autonomous Vehicle Sensor Fusion
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Solution Overview
Problem
Current methods for training machine learning modules for autonomous vehicles are resource-intensive, particularly in acquiring and processing data from multiple sensory modalities like cameras and radar, as there is no efficient way to leverage existing data from one modality to train systems using another modality, leading to a bottleneck in data acquisition and utilization.
Innovation Solution
A method using a generator within a conditional generative adversarial network (GAN) to translate data from one sensory modality (e.g., camera) into synthetic data for another modality (e.g., radar), optimizing trainable parameters to ensure consistency and similarity, allowing for the use of existing data across different sensory spaces, thereby reducing the resource burden of data acquisition and enhancing the capability of machine learning modules to recognize objects across various modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple imaging modalities are provided at the same vehicle to cover all possible circumstances, then the observation completeness is improved, but the device complexity and resource consumption increase
Solution Approach 1:
The patent generates synthetic measured data that copies the characteristics of real measured data from one modality and transforms them into another modality. This allows the system to replicate observations from camera modality into radar modality and vice versa, effectively adding observational coverage without physically installing multiple sensors for every possible circumstance.
Solution Approach 2:
The patent introduces a translation model as an intermediary that converts measured data between different physical observation modalities. This mediator enables the system to leverage existing multi-modality data by translating camera data to radar representations and back, thereby achieving comprehensive observation coverage without directly deploying all possible sensor types simultaneously.
2Quantity of substance
If real measured data from multiple modalities are collected to train machine learning modules, then the training data quantity is improved, but the loss of time and resource consumption increase
Solution Approach 1:
The patent performs preliminary translation of real measured data from one modality into synthetic data for another modality before the actual training process. By pre-translating and prepping multi-modality training data through the translation model, the system can efficiently generate diverse training samples without requiring time-consuming field collection for each modality separately.
Solution Approach 2:
The translation model creates synthetic copies of real measured data that replicate the statistical properties and spatial relationships of the original data while transforming it into a different modality. This copying approach enables rapid generation of abundant training data for underrepresented modalities without requiring equivalent physical data collection efforts.
3Productivity
If training data for a specific modality are used to train machine learning modules, then the training efficiency is improved, but the adaptability to process multiple modalities decreases
Solution Approach 1:
The translation model serves multiple functions: it translates data between modalities, generates synthetic training data, and enables a single machine learning module to process multiple observation modalities. This universal component allows the system to maintain training efficiency for each modality while achieving cross-modality adaptability through a unified translation framework.
Solution Approach 2:
The translation model acts as an intermediary layer that enables efficient training with single-modality data while simultaneously providing multi-modality processing capability. By translating data through this intermediary, the system can train efficiently on one modality and then adapt to process other modalities without requiring separate training pipelines for each modality.
Data Source
AI summary
A method for training a generator. The generator is supplied with at least one actual signal that includes real or simulated physical measured data from at least one observation of the first area. The actual signal is translated by the generator into a transformed signal that represents the associated synthetic measured data in a second area. Using a cost function, an assessment is made concerning to what extent the transformed signal is consistent with one or multiple setpoint signals, at least one setpoint signal being formed from real or simulated measured data of the second physical observation modality for the situation represented by the actual signal. Trainable parameters that characterize the behavior of the generator are optimized with the objective of obtaining transformed signals that are better assessed by the cost function. A method for operating the generator, and that encompasses the complete process chain are also provided.