Driving Scenario Retrieval Using Annotated Locomotion Concepts
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Solution Overview
Problem
Current driving scenario simulations for autonomous and semi-autonomous vehicles are limited by the lack of comprehensive and diverse scenarios, particularly in regards to weather conditions, traffic conditions, driving behaviors, and organizational storage methods, which hinders vehicle safety testing and validation.
Innovation Solution
A computing system that generates, organizes, and stores searchable driving scenarios, using annotated data to infer mappings between scenario data and concepts associated with vehicle locomotion, enabling the retrieval of relevant scenarios based on queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving scenarios are stored in an organized, searchable manner with comprehensive annotations, then the usability and usefulness of scenarios for safety testing is improved, but the complexity of data processing and storage infrastructure increases
Solution Approach 1:
The patent segments driving scenario data into structured components including annotated frames, scenario metadata, and concept labels. Each scenario is divided into discrete elements (entities, environmental conditions, driving behaviors) that can be independently processed and searched, reducing overall system complexity while improving reliability.
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically annotates and tags driving scenario data with standardized concepts and metadata. This intermediary system acts as a mediator between raw driving data and the search/query interface, managing complexity internally while providing simplified access to users.
2Adaptability or versatility
If diverse driving scenarios incorporating weather conditions, traffic conditions, and driving behaviors are generated, then the comprehensiveness of safety testing is improved, but the time and resources required for scenario generation and processing increase
Solution Approach 1:
The patent applies preliminary action by pre-generating and pre-annotating diverse driving scenarios with all possible variations of weather conditions, traffic conditions, and driving behaviors before actual safety testing begins. Scenarios are prepared in advance with complete annotations, eliminating the need for time-consuming processing during testing phases.
Solution Approach 2:
The patent systematically varies parameters such as weather conditions (rain, snow, fog), traffic conditions (density, flow patterns), and driving behaviors (aggressive, defensive, normal) to generate diverse scenarios. By changing these parameters in a structured manner, the system achieves high scenario diversity while maintaining efficient generation through parameterized templates.
3Measurement precision
If extensive manual annotation and organization of driving scenarios is performed, then the precision of scenario data and accuracy of concept mapping is improved, but the labor costs and processing time increase
Solution Approach 1:
The patent implements self-service through automated annotation systems that use machine learning and computer vision to automatically tag and label driving scenario data. The system annotates scenarios itself without requiring extensive manual human intervention, maintaining high precision through algorithmic consistency while dramatically improving processing throughput.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated computational systems. Machine learning models and automated image processing algorithms substitute human annotators, maintaining or improving annotation precision while increasing productivity by eliminating manual labor bottlenecks.
Data Source
AI summary
A system includes one or more processors that obtain annotated frames of data. The annotated frames represent or are associated with a locomotive concept and include annotations. The system infers mappings between the annotated frames and concepts associated with locomotion of the vehicle. Each of the mappings correlates a subset of the annotated frames with a concept. The system receives a query for a particular concept, and retrieves, based on the mappings, a particular subset of the annotated frames correlated with the particular concept.