Autonomous Vehicle Sensor Embeddings for Fast Similarity Search
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Solution Overview
Problem
Autonomous vehicle sensor data repositories face challenges in efficiently searching high-dimensional sensor data due to computational resource consumption and difficulty in identifying similar samples, especially when samples do not match pre-existing labels or have noisy labels.
Innovation Solution
Implementing embedding neural networks to reduce data dimensionality, allowing for efficient search of sensor data repositories by generating embeddings that represent sensor samples' characteristics, and using indexing techniques like slicing to optimize search processes and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is stored in high-dimensional format for detailed representation, then measurement precision is improved, but computational resource consumption increases
Solution Approach 1:
The patent transforms high-dimensional sensor data into lower-dimensional embedding vectors while preserving semantic meaning. This dimensionality reduction allows efficient storage and search operations without completely losing the detailed information, resolving the contradiction between representation accuracy and computational resource consumption.
Solution Approach 2:
The patent introduces embedding vectors as an intermediary representation between the original high-dimensional sensor data and the search operations. These embeddings serve as a compressed intermediate form that enables efficient computation while maintaining the essential characteristics needed for similarity search.
2Adaptability or versatility
If sensor data repository size increases to store more samples, then adaptability is improved, but search time increases
Solution Approach 1:
By converting sensor data to embedding vectors in a lower-dimensional space, the patent enables efficient search operations even as the repository grows. The reduced dimensionality allows for faster distance calculations and similarity comparisons, maintaining search performance despite increased repository size and improved adaptability.
3Measurement precision
If traditional search methods are used on high-dimensional sensor data, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The patent introduces embedding vectors as an intermediary that simplifies the search process. Instead of directly searching high-dimensional sensor data with complex algorithms, the system uses pre-computed embeddings that capture essential features, reducing the complexity of the search system while maintaining measurement precision through the quality of the embedding representation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching an autonomous vehicle sensor data repository. One of the methods includes maintaining a collection of sensor samples and, for each sensor sample, an embedding of the sensor sample; receiving a request specifying a query sensor sample, wherein the query sensor sample characterizes a query environment region; and identifying, from the collection of sensor samples, a plurality of relevant sensor samples that characterize similar environment regions to the query environment region, comprising: processing the query sensor sample through the embedding neural network to generate a query embedding; and identifying, from sensor samples in a subset of the sensor samples in the collection, a plurality of sensor samples that have embeddings that are closest to the query embedding.