Schema-Based Environment Encoding for Reliable Hazard Scenario Clustering
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Solution Overview
Problem
Conventional approaches to interpreting and applying environmental information for vehicles are limited by their unstructured nature, leading to inaccurate scenario classification and unreliable hazard identification due to high dimensionality of feature data, which can result in undesired consequences such as misclassification of scenarios and hazards.
Innovation Solution
The use of schema-based encodings that provide a structured representation of environmental information, including elements for agents, motion information, potential interactions, and metadata, allowing for clustering and labeling of scenarios, and enabling more accurate and reliable scenario classification through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional unstructured approaches are used to interpret environmental information, then the system can process high-dimensional feature data, but scenario classification accuracy and hazard identification reliability deteriorate
Solution Approach 1:
The patent segments the complex environmental information into structured schema-based representations with defined elements (agents, motions, interactions, metadata). This segmentation organizes high-dimensional feature data into manageable, categorized components, improving classification accuracy while reducing the effective complexity through systematic organization.
Solution Approach 2:
The patent transforms environmental information from unstructured raw data into structured schema-based encodings by changing the representation parameters. This transformation applies standardized schemas that convert variable-dimensional feature data into consistent structured formats, enabling reliable hazard identification despite the original data complexity.
2Reliability
If unstructured environmental information is processed, then data collection is simpler, but scenario classification becomes inaccurate and unreliable
Solution Approach 1:
The patent applies preliminary action by pre-defining schemas for environmental information representation before processing actual data. These predefined schemas establish the structure for agents, motions, interactions, and metadata in advance, ensuring reliable hazard identification while maintaining processing simplicity through template-based approaches.
Solution Approach 2:
The structured schema acts as an intermediary between raw sensor data and scenario classification algorithms. This intermediary layer organizes environmental information into standardized formats, bridging the gap between simple data collection and reliable classification without requiring complex processing pipelines.
3Loss of information
If high-dimensional feature data is used, then comprehensive environmental information is captured, but scenario classification accuracy decreases due to unstructured nature
Solution Approach 1:
The patent segments comprehensive environmental information into distinct structured categories (agents, motions, interactions, metadata), preserving all captured information while organizing it for accurate classification. Each segment maintains its informational content while contributing to a structured whole that improves measurement precision.
Solution Approach 2:
The patent transforms high-dimensional unstructured feature data into structured schema-based representations by adding a structural dimension. This dimensionality change organizes the data along new axes (element types, relationships, metadata categories), enabling accurate scenario classification while preserving the completeness of the original environmental information.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can access a plurality of schema-based encodings providing a structured representation of an environment captured by one or more sensors associated with a plurality of vehicles traveling through the environment. The plurality of schema-based encodings can be clustered into one or more clusters of schema-based encodings. At least one scenario associated with the environment can be determined based at least in part on the one or more clusters of schema-based encodings.