Sensor Feature Selection for Resource-Efficient Classification Models
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Solution Overview
Problem
Autonomous vehicles require efficient classification models that utilize a reduced set of features from sensor data to minimize computing resources without compromising accuracy, as conventional models consume excessive memory and bandwidth due to large input vectors.
Innovation Solution
A method to identify a subset of features by computing influence scores using backpropagation, excluding less influential features like noise, and training a second model on this reduced set, thereby reducing resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of features are used to train the computer-implemented model, then classification accuracy is improved, but computing resources and memory consumption increase
Solution Approach 1:
The patent extracts and removes less influential features from the feature set through automated feature selection. The system evaluates feature importance and excludes features that contribute minimally to classification accuracy, thereby reducing the input vector size and computing resource requirements while preserving the essential information needed for accurate object classification in autonomous vehicle environments
Solution Approach 2:
The patent changes the parameter of feature dimensionality by selecting a reduced subset of features from the original large set. Through automated feature selection processes, the system transforms the input from a high-dimensional feature space to a lower-dimensional space that maintains classification performance while reducing computational complexity and memory consumption
2Measurement precision
If a large number of features are used to train the computer-implemented model, then classification accuracy is improved, but memory consumption increases
Solution Approach 1:
The patent extracts and removes less influential features from the feature set through automated feature selection. The system evaluates feature importance and excludes features that contribute minimally to classification accuracy, thereby reducing the input vector size and computing resource requirements while preserving the essential information needed for accurate object classification in autonomous vehicle environments
Solution Approach 2:
The patent changes the parameter of feature dimensionality by selecting a reduced subset of features from the original large set. Through automated feature selection processes, the system transforms the input from a high-dimensional feature space to a lower-dimensional space that maintains classification performance while reducing computational complexity and memory consumption
3Measurement precision
If a large number of features are transmitted from sensor system to computing system, then classification accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The patent extracts and removes less influential features from the feature set through automated feature selection. The system evaluates feature importance and excludes features that contribute minimally to classification accuracy, thereby reducing the input vector size and computing resource requirements while preserving the essential information needed for accurate object classification in autonomous vehicle environments
Solution Approach 2:
The patent changes the parameter of feature dimensionality by selecting a reduced subset of features from the original large set. Through automated feature selection processes, the system transforms the input from a high-dimensional feature space to a lower-dimensional space that maintains classification performance while reducing computational complexity and memory consumption
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach maintains classification accuracy while significantly reducing computing resources, memory, and bandwidth needs, enabling more efficient execution and potentially cheaper sensor deployment.
Implementation Method 1
computing gradients corresponding to the input values by backpropagating an output score through the trained first computer-implemented neural network
Data Source
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AI summary
Technologies pertaining to identifying a set of features to use when training a computer-implemented model are described. A first computer-implemented model is trained to perform a classification task based upon values for features in a set of features, where the features include features of sensor data and a noise feature. An importance score is computed for each feature in the set of features, and a subset of the set of features is identified based upon the importance scores computed for the features. A second computer-implemented model is then trained to perform the classification task based upon values for the features in the subset of features.