Road Element Topic Representation for Low-Resource Classification
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Solution Overview
Problem
Vehicles with autonomous driving capabilities face challenges in efficiently processing and classifying high-dimensional sparse binary representations of road elements, leading to increased memory and processing resource demands.
Innovation Solution
A method utilizing decorrelated topic-based representation of road elements through unsupervised learning and correlation explanation to reduce the processing and storage resources required for classification, by identifying latent factors that minimize redundancy in data and summarize it more compactly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high dimensional sparse binary representation is used for road element classification, then classification accuracy is improved, but memory and processing resource demands increase
Solution Approach 1:
The patent extracts only the necessary topic information from the high dimensional sparse binary representation. Instead of processing the entire high-dimensional data, the system identifies and extracts relevant topic bits that are actually needed for classification, thereby reducing processing resources while maintaining accuracy.
Solution Approach 2:
The patent segments the high dimensional sparse binary representation into multiple topics, where each topic represents a specific aspect of road elements. This segmentation allows the system to process and classify road elements by focusing on individual topics rather than handling the entire high-dimensional data at once, reducing computational complexity.
2Measurement precision
If high dimensional sparse binary representation is used for road element classification, then classification accuracy is improved, but memory resources increase
Solution Approach 1:
The system extracts only the essential topic information from the high dimensional representation, storing and processing only what is necessary for classification. This extraction approach significantly reduces memory requirements while preserving the classification accuracy that depends on the most important features.
Solution Approach 2:
The patent applies partial action by processing only a fraction of the high dimensional data - specifically, only the topic information that is relevant for classification. This partial processing approach maintains sufficient accuracy for practical applications while dramatically reducing memory consumption compared to processing the complete high-dimensional representation.
Data Source
AI summary
A method decorrelated topic based representation of road elements for classification, the method includes obtaining, at a machine learning process, a sparse binary representation corresponding to an initial embedding space of a road element captured in a sensed information unit; and selecting, by checking values of topic information of the sparse binary representation using the machine learning process, a topic, from a set of topics respectively characterized in the initial embedding space, the selected topic corresponding to a reduced space, each of the selected set of topics determined in decorrelation from another topic based on at least in part a measurement of an entropy distribution of bits of the sparse binary representation.