Vehicle Data Collection with Price-List Validation for AI/ML
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Solution Overview
Problem
Existing AI/ML model training and testing face challenges in accessing high-quality data under specific conditions or geographical limitations, leading to data relevancy and specificity issues, which can result in inaccurate models and overfitting.
Innovation Solution
A system and method for collecting data from vehicles, involving data validation and compensation based on a price list, to ensure data quality and contribution to AI/ML model improvements, including performance, test coverage, and feature map coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected from vehicles across diverse geographical locations, then data diversity and model representativeness are improved, but data quality validation and compensation accuracy become more complex
Solution Approach 1:
The system segments the data collection process into distinct stages: data receipt, validation against price list criteria, contribution assessment, and compensation calculation. This segmentation allows each stage to be handled independently with specialized logic, making the overall complex system more manageable and maintainable while ensuring comprehensive data diversity validation.
Solution Approach 2:
The system implements feedback mechanisms where the validated data is used to train AI/ML models, and the model performance improvements are fed back to determine appropriate compensation levels. This closed-loop feedback ensures that compensation accurately reflects the actual contribution of diverse data to model performance, resolving the complexity of fair compensation across different data types and locations.
2Reliability
If data validation is performed to ensure high quality, then model accuracy is improved, but time and computational resources are increased
Solution Approach 1:
The system performs preliminary validation of data against predefined price list criteria before the data is used for model training. This preliminary action filters out low-quality data early in the process, preventing time-consuming validation and compensation calculations for data that would not contribute to model improvements anyway.
Solution Approach 2:
The system replaces manual or complex validation mechanisms with automated validation algorithms that compare collected data against predefined price list criteria. This substitution significantly reduces validation time and computational resources while maintaining high data quality standards through consistent, rule-based assessment.
3Measurement precision
If compensation is based on data contribution to model improvement, then fair payment to data providers is achieved, but determining contribution requires extensive analysis
Solution Approach 1:
The system uses feedback from model performance metrics to determine data contribution levels. By tracking how validated data affects model accuracy, test coverage, and feature map coverage, the system automatically calculates compensation levels that accurately reflect each data provider's contribution, eliminating the need for complex manual analysis while ensuring fair payment.
Solution Approach 2:
The system translates complex contribution assessment into measurable parameters such as model performance improvement percentages, test coverage ratios, and feature map coverage areas. By changing the assessment focus to these quantifiable parameters, the system simplifies contribution determination while maintaining precise measurement of data value for compensation purposes.
4Productivity
If generic or insufficient specific data is used due to availability limitations, then data collection effort is reduced, but model precision and real-world representativeness deteriorate
Solution Approach 1:
The system creates a universal data collection framework that can acquire diverse data types from multiple sources (vehicles, sensors, cameras) across different geographical locations. This multi-functional approach enables the system to efficiently gather both specific and generic data, with the validation process ensuring that even generic data meets quality standards for maintaining model precision.
Solution Approach 2:
The system dynamically adjusts data collection parameters based on availability and requirements. When specific data is unavailable, the system can collect alternative data and adjust validation criteria to maintain sufficient precision. This parameter flexibility allows the system to maintain model precision while adapting to varying data availability conditions across different regions and scenarios.
Data Source
AI summary
Example embodiments of the present disclosure relate to efficiency and effectively collect data for artificial intelligence (AI)/Machine Learning (ML) models. According to example embodiments, a method, performed by a system for collecting data from a vehicle, is provided. The method may include: receiving data from the vehicle; obtaining a price list; validating the data based on the price list; based on determining that the data is validated, determining whether or not the data contributes to one or more improvements of at least one AI/ML model; based on determining that the data contributes to the improvement of the one or more performances, determining a price for compensating a user associated with the vehicle; and compensating the user based on the determined price.