Autonomous Vehicle Training Data Management System
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Solution Overview
Problem
The challenge lies in managing and updating training data for neural networks in autonomous vehicles, where changes in one component can affect others, and the increasing volume of data makes storage and processing inefficient, especially when determining which data to use for training and ensuring all components operate with the latest information.
Innovation Solution
A computing system that generates, stores, and deletes data associated with different versions of components, identifies affected components in a hierarchical relationship, and regenerates training data to ensure all components are updated, using log data from sensors and simulations to improve accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all available training data is stored, then training accuracy can be improved, but storage requirements increase significantly
Solution Approach 1:
The system performs preliminary analysis of data dependencies before training, identifying which components will be affected by training data changes. This allows selective generation of training data only for affected components rather than processing all available data, reducing storage requirements while maintaining training accuracy for relevant components.
Solution Approach 2:
Instead of uniformly processing all training data for all components, the system applies local quality by identifying and processing only the specific subsets of training data that are relevant to each affected component. This targeted approach reduces overall storage requirements while ensuring each component receives appropriate training data.
2Reliability
If training data is generated for all components, then all components can be updated, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary identification of affected components by analyzing data flow relationships and component dependencies before initiating training data generation. This preliminary action enables selective processing only for components that will actually benefit from the training data changes, reducing processing time while ensuring all affected components are updated.
Solution Approach 2:
The system segments the component set into affected and unaffected groups based on data flow analysis. Training data generation is then performed only for the affected segment, while the unaffected segment is skipped. This segmentation maintains update completeness for relevant components while significantly reducing overall processing time.
3Measurement precision
If manual determination of training data selection is performed, then data quality can be controlled, but system complexity and operational effort increase
Solution Approach 1:
The system implements self-service by automatically analyzing component dependencies, identifying affected components, and determining relevant training data subsets without manual intervention. The automated dependency analysis and selective data generation maintain data quality while reducing system complexity and operational effort compared to manual determination approaches.
4Adaptability or versatility
If components are updated independently, then update flexibility is maintained, but data consistency across the system deteriorates
Solution Approach 1:
The system implements feedback mechanisms by analyzing data flow relationships between components and using this information to determine which components are affected by training data changes. This feedback-driven approach ensures that when one component is updated, all dependent components are identified and updated accordingly, maintaining data consistency while preserving update flexibility through selective processing.
Solution Approach 2:
The system creates a universal dependency analysis framework that can be applied across different component types and update scenarios. This universal approach identifies affected components regardless of their specific type or position in the system, ensuring data consistency is maintained across the entire system while allowing flexible updates to individual components.
Data Source
AI summary
Techniques for managing data associated with different versions of components over time by a computing system are discussed herein. The computing system can generate, store, and/or delete data associated with different versions of components having a hierarchical relationship such that data can be fed from one component to another component over time). The computing system can receive log data associated with one or more components and determine regeneration data for different components of a vehicle computing device over time. The computing system can include a user interface for receiving file generation requests to test, validate, or verify functionality of a particular component of the vehicle computing device at different instances of time.