Scenario Information Embeddings for Taxonomy-Free Similarity Search
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Solution Overview
Problem
Conventional approaches for organizing and searching scenario information in vehicles are inefficient, often leading to incomplete or inaccurate computer-based simulations due to reliance on rigid taxonomies that require developers to manage and update complex categorization structures, and fail to identify relevant scenarios without thorough understanding of the taxonomy.
Innovation Solution
A machine learning-based approach that generates embeddings in a vector space for scenario representations, allowing for the identification of similar scenarios through encoded images and non-image queries, independent of hierarchical categorization, using techniques like triplet loss and nearest-neighbor search algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rigid taxonomy structures are used for organizing scenario information, then scenario categorization is systematic and manageable, but scenario retrieval becomes inefficient and requires thorough understanding of the taxonomy
Solution Approach 1:
The patent replaces the mechanical taxonomy-based search system with a machine learning embedding system. Instead of manually navigating hierarchical categories, scenarios are transformed into vector representations where semantic similarity is captured geometrically. The search process substitutes manual taxonomy traversal with automated vector space operations, enabling efficient retrieval without requiring users to understand complex categorization structures.
Solution Approach 2:
The patent transforms scenario information from structured categorical parameters into continuous vector embeddings. This parameter transformation allows scenarios to be represented in a continuous vector space where similarity is determined by geometric distance rather than discrete category matching. The embedding process converts rigid taxonomic parameters into flexible continuous representations that capture nuanced semantic relationships.
2Reliability
If comprehensive scenario coverage is achieved through detailed taxonomy, then simulation accuracy improves, but search and retrieval time increases
Solution Approach 1:
The patent performs preliminary transformation of all scenarios into vector embeddings during an offline preprocessing stage. This advance preparation creates ready-to-use vector representations that can be rapidly queried without requiring complex taxonomy traversal during actual search operations. The computationally intensive embedding process is moved to beforehand, enabling fast online retrieval while maintaining comprehensive scenario coverage.
Solution Approach 2:
The patent creates vector embedding copies of scenario representations that preserve semantic information in a compact, searchable format. Instead of storing and searching through detailed taxonomy structures, the system maintains vector copies that capture essential scenario characteristics. These embedding copies enable rapid similarity-based retrieval while the original comprehensive scenario data remains available for accurate simulation.
3Adaptability or versatility
If developers manually manage taxonomy updates, then scenario categorization remains organized, but system adaptability to new scenarios decreases
Solution Approach 1:
The patent implements a self-service system where new scenarios are automatically processed through the embedding pipeline without requiring manual taxonomy updates. When new scenarios are introduced, the machine learning model automatically generates their vector representations based on learned patterns from existing data. This self-service capability enables the system to adapt to new scenario types without human intervention in taxonomy management, while the embedding space naturally organizes them according to semantic similarity.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can receive a query specifying at least one example scenario. At least one image representation of the at least one example scenario can be encoded based on the query to produce at least one encoded representation. An embedding of the at least one representation of the at least one example scenario can be generated based on the at least one encoded representation. At least one scenario that is similar to the at least one example scenario can be identified based at least in part on the embedding of the at least one representation of the at least one example scenario and an embedding representing the at least one scenario. Information describing the at least one identified scenario can be provided in response to the query.