Guided Domain Adaptation for Sensor Feature Alignment
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Solution Overview
Problem
Existing machine-learning models face challenges in adapting to new sensor suites due to differences in intrinsic and extrinsic parameters, leading to overfitting and poor performance on live data from different sources.
Innovation Solution
The system performs guided domain adaptation by determining a subset of source features based on similarity with target features and aligning the target features with this subset, using source and target semantic attributes and trajectory information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional domain adaptation techniques are used to align features from different sensor suites, then the model can be applied to new sensors, but the model overfits to source data and performs poorly on live data from different sources
Solution Approach 1:
The patent extracts only the most similar source features that match target semantic attributes, rather than aligning all source features. This selective extraction prevents overfitting by focusing only on relevant features that generalize well to target domains while discarding source-specific features that would cause overfitting.
Solution Approach 2:
The patent applies different alignment strategies to different feature subsets based on their similarity to target semantic attributes. High-similarity features receive full alignment while low-similarity features are excluded, creating local quality variations in the alignment process that improve overall generalization.
2Quantity of substance
If all source features are aligned with target features, then comprehensive feature coverage is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts and aligns only the subset of source features that have semantic similarity to target features, rather than processing all source features. This reduces the quantity of features requiring alignment while maintaining comprehensive coverage of relevant semantic attributes.
Solution Approach 2:
The patent performs partial alignment by focusing only on the necessary subset of features that match target semantic attributes, rather than exhaustively aligning all possible source features. This partial action achieves sufficient feature coverage with reduced computational complexity.
3Speed
If feature alignment is performed without semantic attribute guidance, then alignment speed is faster, but alignment accuracy and generalization performance deteriorate
Solution Approach 1:
The patent performs preliminary computation of semantic attribute similarities between source and target features before the actual alignment process. This pre-computed similarity information guides the subsequent alignment, ensuring high accuracy while maintaining speed by avoiding complex iterative optimization during alignment.
Solution Approach 2:
The patent introduces semantic attributes as an intermediary that mediates the alignment process between source and target features. The semantic similarity scores act as a guiding intermediary that enables accurate and efficient alignment by providing a clear criterion for feature matching without requiring complex direct feature comparison.
Data Source
AI summary
Systems and techniques are described herein for processing data. For instance, a method for processing data is provided. The method may include obtaining source features generated based on first sensor data captured using a first set of sensors; obtaining source semantic attributes related to the source features; obtaining target features generated based on second sensor data captured using a second set of sensors; obtaining map information; obtaining location information of a device comprising the second set of sensors; obtaining target semantic attributes from the map information based on the location information; aligning the target features with a set of the source features, based on the source semantic attributes and the target semantic attributes, to generate aligned target features; and processing the aligned target features to generate an output.