Autonomous Vehicle Sensor Search Using Context Embeddings
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently searching for similar scenarios in sensor data repositories, which often involve complex relationships between multiple objects and their contexts over time, making it difficult to identify relevant training data for machine learning models.
Innovation Solution
The system utilizes context embeddings, specifically spatio-temporal and vehicle intent embeddings, generated by neural networks to characterize and index sensor data, allowing for effective similarity search through lower-dimensional representations, enabling the identification of relevant scenarios without relying on accurate object recognition or rule-based methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is stored in high-dimensional raw format for accurate representation, then measurement precision is improved, but device complexity and search efficiency deteriorate
Solution Approach 1:
The patent extracts essential semantic features from high-dimensional sensor data by generating context embeddings that capture the meaning and relationships in the data. This extraction process creates a condensed representation that maintains measurement precision while reducing the complexity of the search system by working with lower-dimensional embedding vectors instead of raw sensor data.
Solution Approach 2:
The patent introduces context embeddings as an intermediary representation between raw sensor data and the search system. These embeddings serve as a mediator that preserves the semantic information needed for accurate search while transforming the data into a format that is computationally efficient and reduces system complexity.
2Loss of information
If comprehensive sensor data from multiple time steps is collected for complete scenario characterization, then information completeness is improved, but loss of time and processing overhead increase
Solution Approach 1:
The patent extracts only the essential temporal and contextual features needed for scenario characterization by generating embeddings from sensor data at multiple time steps. This extraction captures the complete scenario information while reducing the time required for data collection and processing by working with condensed embedding representations rather than raw multi-temporal data.
Solution Approach 2:
The patent performs preliminary processing of sensor data by generating context embeddings in advance, which captures the temporal relationships and scenario characteristics before search operations are performed. This preliminary action prepares the data in a ready-to-search format that maintains information completeness while reducing processing time during actual search operations.
3Measurement precision
If rule-based methods are used for scenario matching, then measurement precision is improved, but adaptability and ease of operation deteriorate
Solution Approach 1:
The patent replaces rule-based matching mechanisms with a neural network-based embedding system. This substitution maintains measurement precision by capturing complex relationships through learned representations while dramatically improving adaptability, as the system can automatically learn new scenario patterns without requiring explicit rule updates.
Solution Approach 2:
The patent changes the fundamental parameters of the matching system from fixed rules to learned embedding representations. This parameter change enables the system to maintain accurate matching while becoming highly adaptable to new scenarios, as the embeddings are learned from data and can automatically adjust to represent new pattern types.
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 one or more embeddings of each sensor sample. Each sensor sample is generated from sensor data at multiple time steps and characterizes an environment at each of the multiple time steps. Each embedding corresponds to a respective portion of the sensor sample and has been generated by an embedding neural network. A query specifying a query portion of a query sensor sample is received. A query embedding corresponding to the query portion of the query sensor sample is generated through the embedding neural network. A plurality of relevant sensor samples that have embeddings that are closest to the query embedding are identified as characterizing similar scenarios to the query portion of the query sensor sample.