Top-Down Driving Scenario Embeddings for Relevant Similarity Search
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Solution Overview
Problem
Conventional systems fail to effectively identify and generate similar driving scenarios for autonomous vehicles, as they rely solely on visual characteristics, missing relevant scenarios that may differ significantly in sensor data analysis and decision-making, and including irrelevant scenarios that appear similar visually.
Innovation Solution
A scenario analysis system that uses a machine-learned model to generate a top-down representation of environments, extracts multi-dimensional vectors from intermediate layers, and applies a proximity search algorithm in a multi-dimensional space to identify similar scenarios based on sensor data, including dynamic and static objects, and their changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scenario classification relies solely on visual characteristics, then scenarios that appear visually similar are grouped together, but relevant scenarios that differ in sensor data analysis and decision-making are missed
Solution Approach 1:
The patent transitions from two-dimensional visual image comparison to multi-dimensional vector space comparison by extracting feature vectors that encode sensor data, object states, and decision-making parameters. This dimensional expansion enables classification based on functional relevance rather than superficial visual similarity.
Solution Approach 2:
The patent changes the classification parameters from visual characteristics (color, shape, position in image) to functional parameters (sensor data patterns, object dynamics, decision-making context). This parameter transformation allows scenarios to be grouped by their operational significance to autonomous vehicle systems.
2Quantity of substance
If scenario classification includes all visually similar scenarios, then comprehensive visual coverage is achieved, but irrelevant scenarios that appear similar visually are included
Solution Approach 1:
The patent changes classification parameters from visual features to functional features derived from sensor data and decision-making processes. This enables filtering of visually similar but functionally irrelevant scenarios while retaining visually different but functionally relevant scenarios.
Solution Approach 2:
The patent replaces traditional image-based visual comparison mechanisms with machine learning model-based functional analysis. The system uses trained models to extract meaningful features from sensor data and simulate decision-making processes, substituting mechanical visual pattern matching with intelligent functional assessment.
3Ease of manufacture
If conventional visual-based scenario identification is used, then implementation is simple, but the system fails to identify relevant scenarios for autonomous vehicle decision-making
Solution Approach 1:
The patent introduces machine learning models as intermediary components between raw sensor data and scenario classification. These models process and transform sensor inputs into meaningful feature vectors that capture functional relationships, enabling accurate scenario identification without requiring complex manual feature engineering.
Solution Approach 2:
The patent replaces simple visual image processing with intelligent machine learning-based analysis. The system uses trained neural networks to extract functional features from sensor data, simulate autonomous vehicle decision-making, and perform scenario matching based on operational relevance rather than visual appearance.
Data Source
AI summary
Techniques for analyzing driving scenarios are discussed herein. For example, techniques may include determining a level of exposure associated with scenarios, searching for similar scenarios, and generating new additional scenarios. A driving scenario may be represented as top-down multi-channel data. The top-down multi-channel data may be provided as input to a neural network trained to output a prediction of future events. A multi-dimensional vector representing the scenario can be received as an intermediate output from the neural network and may be stored to represent the scenario. Multi-dimensional vectors representing different scenarios may be stored in a multi-dimensional space, and similar scenarios may be identified by proximity searching of the multi-dimensional space.