Scenario Embedding Search for Vehicle Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional approaches for retrieving scenario information in vehicles are inefficient due to reliance on hierarchical taxonomies, which can lead to incomplete or inaccurate scenario retrieval, especially when scenarios are not fully represented by existing categories, placing a burden on developers and requiring continuous updates to understand and manage taxonomy structures.
Innovation Solution
A machine learning-based approach that generates embeddings for scenarios in a vector space, allowing for the identification of similar scenarios without relying on hierarchical structures, using encoded images from various sensors and semantic map information, and enabling searches through high-level primitives and natural language queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If hierarchical taxonomy structures are used for scenario retrieval, then scenario information can be organized systematically, but retrieval completeness and accuracy deteriorate when scenarios do not fit existing categories
Solution Approach 1:
The patent replaces the mechanical hierarchical taxonomy system with a machine learning-based embedding system. Scenarios are transformed into vector representations that capture semantic meaning, allowing similarity-based retrieval without rigid categorical constraints. This substitution enables the system to handle novel or unclassified scenarios effectively while maintaining organized access to scenario data.
Solution Approach 2:
The patent changes the fundamental parameter for scenario organization from discrete hierarchical categories to continuous vector space embeddings. By representing scenarios as points in a high-dimensional vector space where distance reflects semantic similarity, the system transitions from categorical matching to gradient-based similarity measurement, improving retrieval accuracy for edge cases.
2Adaptability or versatility
If hierarchical taxonomy structures are continuously updated to accommodate new scenarios, then scenario coverage improves, but developer burden and system complexity increase
Solution Approach 1:
The patent implements a self-service mechanism where the machine learning model automatically adapts to new scenarios through embedding generation without requiring manual taxonomy updates. The system autonomously handles scenario classification and retrieval by computing vector representations, eliminating the need for developers to continuously restructure hierarchical categories while maintaining comprehensive scenario coverage.
Solution Approach 2:
The patent performs preliminary action by pre-training the machine learning model on diverse scenario data before deployment. This pre-training enables the system to handle a broad range of scenarios out-of-the-box, reducing the need for subsequent manual taxonomy updates and allowing the system to adapt to new scenarios through the inherent capabilities of the trained model.
3Measurement precision
If detailed hierarchical categories are created to represent all possible scenarios, then scenario classification precision improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The patent replaces complex manual hierarchical classification with automated machine learning-based embedding. The system automatically computes vector representations that capture nuanced scenario characteristics, achieving high classification precision without requiring users to navigate complex hierarchical structures. This substitution maintains precision while dramatically improving ease of operation.
Solution Approach 2:
The patent introduces vector embeddings as an intermediary between raw scenario data and retrieval operations. These embeddings serve as a bridge that automatically captures semantic relationships and similarities, eliminating the need for users to manually navigate hierarchical categories while maintaining precise scenario classification and retrieval.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can receive a search query including one or more high-level primitives. One or more low-level parameters describing behavior of at least one agent associated with at least one value that satisfies at least one annotation rule associated with the one or more high-level primitives can be determined. In response to determining that the at least one value satisfies the at least one annotation rule, one or more scenarios associated with the one or more low-level parameters that satisfy the at least one annotation rule can be identified by using the one or more high-level primitives included in the search query. Information describing the one or more identified scenarios in response to the search query can be provided.