Viewpoint Augmentation for Autonomous Vehicle Sensor Data
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Solution Overview
Problem
Autonomous vehicles face challenges in training machine-learning models to accurately process sensor data from varying poses and sensor types, leading to performance degradation when viewpoints or sensor characteristics change.
Innovation Solution
The system generates training data for machine-learning models by transforming existing data to simulate different viewpoints and sensor characteristics, using a machine-learning model to warp images and update model parameters based on loss functions, enabling robustness to various contexts without manual recapture of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine-learning models are trained using sensor data captured at fixed poses, then training data collection is simplified, but model generalization to different viewpoints deteriorates
Solution Approach 1:
The patent uses neural radiance fields to create synthetic copies of sensor data from fixed poses, generating transformed images that simulate different viewpoints. This allows the system to generate training data for multiple poses without physically capturing data from each pose, thus maintaining ease of data collection while improving model generalization.
Solution Approach 2:
The system changes the viewpoint parameters of existing sensor data by using neural radiance fields to generate transformed images from different perspectives. This allows training data to be generated for various poses by modifying the viewing parameters rather than physically repositioning sensors, resolving the contradiction between data collection simplicity and model adaptability.
2Adaptability or versatility
If training data is manually recaptured at various poses to improve model generalization, then model adaptability improves, but time and resource requirements increase
Solution Approach 1:
Instead of manually recapturing data at various poses, the system creates synthetic copies of the original sensor data using neural radiance fields. The viewpoint augmentation module generates transformed images that simulate different poses, eliminating the need for time-consuming manual data recapture while maintaining model generalization.
Solution Approach 2:
The system performs preliminary processing of sensor data by training neural radiance fields on the original fixed-pose data. This preliminary action creates a computational model that can then rapidly generate training data for any desired pose, saving the time that would otherwise be required for manual recapture.
3Ease of operation
If sensors are positioned at fixed poses for data collection, then data collection process is simplified, but model performance on varying sensor poses deteriorates
Solution Approach 1:
The patent uses neural radiance fields to create synthetic copies of sensor data that simulate different sensor poses. This allows the system to maintain simple fixed-pose data collection while generating training data that teaches the model to handle varying sensor positions, thus preserving ease of operation while improving model reliability.
4Adaptability or versatility
If viewpoint augmentation is performed to improve model robustness, then model adaptability improves, but computational complexity increases
Solution Approach 1:
The patent replaces the mechanical approach of physically repositioning sensors with a computational approach using neural radiance fields. The viewpoint augmentation module uses learned representations to generate transformed images, substituting complex physical data collection with efficient computational processing that achieves the same goal with reduced overall complexity.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to synthetic data generation using viewpoint augmentation for autonomous and semi-autonomous systems and applications. One or more circuits can identify a set of sequential images corresponding to a first viewpoint and generate a first transformed image corresponding to a second viewpoint using a first image of the set of sequential images as input to a machine-learning model. The one or more circuits can update the machine-learning model based at least on a loss determined according to the first transformed image and a second image of the set of sequential images.