Distributed Synthetic Data Evaluation for Perception Quality
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Solution Overview
Problem
The evaluation of synthetic data quality for perception functions is complex, inefficient, and often prevents its effective use in training or testing, particularly due to manual and iterative processes that hinder scalability and collaboration between data providers and consumers.
Innovation Solution
A method utilizing a distributed system with interfaces for accessing evaluation components, defining applications, generating synthetic data, and evaluating it against reference data using metrics to ensure quality, allowing for automated optimization and collaboration among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual and iterative evaluation processes are used for synthetic data quality assessment, then evaluation thoroughness can be maintained, but productivity and scalability deteriorate
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with automated computer-based systems. The evaluation component automatically executes quality metrics and comparisons between synthetic and reference data without human intervention, substituting the mechanical manual assessment with an automated computational system that maintains thoroughness while dramatically increasing speed and scalability.
Solution Approach 2:
The evaluation system performs self-assessment by automatically comparing synthetic data against reference data using predefined quality metrics. The system serves itself by autonomously executing evaluation protocols, generating quality scores, and identifying areas for improvement without requiring external manual analysis, thereby maintaining comprehensive evaluation while enhancing productivity.
2Measurement precision
If complex individual evaluation processes are implemented by users, then evaluation accuracy can be improved, but device complexity and ease of operation worsen
Solution Approach 1:
The evaluation component is designed as a universal system that handles multiple evaluation tasks through a single integrated interface. It can assess various types of synthetic data (images, videos, sensor data) using the same core infrastructure, supporting multiple quality metrics and comparison methods without requiring separate complex processes for each data type, thereby maintaining accuracy while reducing overall system complexity.
Solution Approach 2:
The evaluation process is segmented into distinct modular components: data loading, quality metric calculation, comparison operations, and result generation. Each module performs a specific function independently, allowing the system to maintain high evaluation accuracy through specialized processing while reducing overall complexity through modular design that can be configured and executed independently.
3Adaptability or versatility
If manual individual evaluation is performed by data consumers, then customization to specific applications is possible, but loss of time and productivity increase
Solution Approach 1:
The evaluation system is designed to be dynamic and configurable, allowing users to select and customize quality metrics and evaluation parameters specific to their application needs. The system can adapt its evaluation protocol dynamically based on the type of synthetic data being assessed and the specific requirements of the target application, maintaining high adaptability while executing the customized evaluation automatically and efficiently.
Solution Approach 2:
The system performs preliminary configuration of evaluation parameters and quality metrics based on the specific application and data type before actual evaluation begins. By pre-configuring the evaluation protocol with application-specific settings, the system eliminates the need for manual setup during the evaluation process itself, thereby maintaining customization capability while significantly reducing the time required for actual data assessment.
4Reliability
If isolated solutions are generated for data, models, tool chains and experiments, then specific problem solving capability is improved, but collaboration and scalability deteriorate
Solution Approach 1:
The patent merges previously isolated evaluation solutions into a unified distributed system architecture. The evaluation component serves as a central hub that can access and evaluate synthetic data from multiple sources while maintaining consistent quality standards across different data providers and consumers. This unified approach preserves the specialized problem-solving capabilities of individual components while enabling seamless collaboration and scaling across the entire ecosystem.
Data Source
AI summary
A method for ensuring a quality of synthetic data for at least one perception function using a distributed system includes (i) providing at least one interface of the distributed system in order as to provide access from at least two instances of the distributed system to an evaluation component of the distributed system by the at least one interface, (ii) determining an application for the synthetic data by one of the at least two instances of the distributed system, wherein the application includes at least the at least one perception function, a description of the synthetic data, and at least one metric for evaluating the quality of the synthetic data, (iii) generating the synthetic data based on the described application by the one or another of the at least two instances of the distributed system, and (iv) evaluating the synthetic data with respect to reference data using the at least one metric by the evaluation component of the distributed system to ensure the quality of the synthetic data. Also disclosed is a computer program, a device and a storage medium for this purpose.