Autonomous Vehicle Scenario Tagging for Log-Based Analytics
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Solution Overview
Problem
Current systems lack an efficient method to determine and analyze autonomous vehicle scenarios from operational data, which hinders performance measurement, simulation testing, and fleet management.
Innovation Solution
A computer-implemented method and system that extracts attributes from log data to identify and categorize scenarios, enabling the generation of autonomous vehicle operation analytics, using a scenario tagger computing system to analyze sensor, map, and motion planning data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to process autonomous vehicle operational data, then detailed scenario analysis can be performed, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated computing system that uses machine learning models and algorithms to extract attributes from log data, identify scenarios, and generate analytics. This substitution dramatically reduces processing time while maintaining or improving analysis accuracy through systematic computational methods.
Solution Approach 2:
The system enables autonomous vehicles to automatically process and analyze their own operational data through onboard computing systems that extract attributes, identify scenarios, and generate analytics without requiring external manual intervention. This self-service capability accelerates the analysis process while preserving detailed scenario information.
2Loss of information
If comprehensive log data is collected from autonomous vehicle operations, then more complete scenario information can be obtained, but data storage and processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant attributes from comprehensive log data using predefined attribute types and extraction rules. This selective extraction process retains essential scenario information while significantly reducing the volume of data that needs to be stored and processed, thereby managing system complexity effectively.
Solution Approach 2:
The system segments log data into distinct attribute types (e.g., vehicle attributes, environmental attributes, scenario attributes) that can be processed independently. This segmentation allows the system to handle comprehensive data systematically through modular processing steps, reducing overall system complexity while maintaining data completeness.
3Measurement precision
If detailed scenario categorization is implemented with multiple variations and features, then more precise performance measurement is enabled, but the complexity of scenario management increases
Solution Approach 1:
The patent introduces a hierarchical dimension to scenario management by organizing scenarios into multiple levels: scenario categories, scenario variations, and scenario features. This multi-dimensional structure enables precise performance measurement across different granularities while managing complexity through systematic organization and abstraction at each level.
Solution Approach 2:
The system creates a universal scenario framework that can accommodate multiple scenario types and variations through a common attribute structure and processing methodology. This universal approach enables precise measurement across diverse scenarios while reducing management complexity by applying consistent rules and patterns throughout the system.
Data Source
AI summary
Systems and methods are directed to determining autonomous vehicle scenarios based on autonomous vehicle operation data. In one example, a computer-implemented method for determining operating scenarios for an autonomous vehicle includes obtaining, by a computing system comprising one or more computing devices, log data representing autonomous vehicle operations. The method further includes extracting, by the computing system, a plurality of attributes from the log data. The method further includes determining, by the computing system, one or more scenarios based on a combination of the attributes, wherein each scenario includes multiple scenario variations and each scenario variation comprises multiple features. The method further includes providing, by the computing system, the one or more scenarios for generating autonomous vehicle operation analytics.