Multi-Source Sensor Dataset Retrieval Through Semantic Embeddings
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Solution Overview
Problem
Existing large datasets for automated driving systems are cumbersome to navigate and access due to reliance on user expertise and static metadata, making it difficult to retrieve relevant data for development and verification.
Innovation Solution
Utilizing embedding networks to generate multi-dimensional vector representations of sensor and auxiliary data samples, enabling connections between different data sources, allowing for free text and image searches across diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large datasets are stored with detailed metadata for comprehensive data coverage, then data completeness is improved, but data accessibility and ease of navigation deteriorate
Solution Approach 1:
The patent introduces embedding vectors as an intermediary layer between the raw large-scale datasets and the query interface. These embeddings transform complex multi-modal data (images, sensor data, auxiliary data) into compressed numerical representations that capture semantic meaning. The embedding database serves as a mediator that enables efficient similarity search without requiring users to navigate the original complex dataset structure, thus resolving the contradiction between maintaining data completeness and improving accessibility.
Solution Approach 2:
The patent extracts essential semantic features from large datasets by generating embedding vectors that represent the core meaning of data samples. Instead of storing and navigating all raw data details, the system extracts key characteristics into compact embedding representations. This extraction process maintains the essential information needed for search and retrieval while eliminating the navigational complexity of the original large-scale dataset.
2Device complexity
If static metadata structures are used for data organization, then data storage efficiency is improved, but flexibility in data retrieval and adaptability to different query types deteriorate
Solution Approach 1:
The patent transforms the data representation from static metadata structures to dynamic embedding vectors. By changing the parameter representation from structured metadata fields to continuous vector spaces, the system enables flexible querying through similarity search. The embedding vectors can capture nuanced relationships and support various query types (text, image, sensor data) without requiring changes to the underlying storage structure, thus resolving the contradiction between storage efficiency and retrieval flexibility.
3Measurement precision
If user expertise and knowledge of data models are required for data access, then data retrieval precision is improved, but ease of operation and usability deteriorate
Solution Approach 1:
The patent implements a self-service retrieval system where the embedding database automatically performs similarity search based on query embeddings. Users simply provide query inputs (text, images, or sensor data) without needing to understand complex data models or retrieval mechanisms. The system autonomously computes similarity metrics and retrieves relevant data samples, eliminating the need for user expertise while maintaining high retrieval precision through the semantic understanding embedded in the vector representations.
Data Source
AI summary
A method for accessing a dataset comprising a plurality of data samples from multiple data sources and related aspects are disclosed. The plurality of data samples includes sensor data samples and auxiliary data samples represented by sensor data embeddings and auxiliary data embeddings, respectively. The method includes in response to obtaining a query embedding representing a query, identifying one or more embeddings within the multi-dimensional vector space based on a proximity to the obtained query embedding within the multi-dimensional vector space, and outputting one or more data samples within the dataset that are represented by the identified embeddings.