Automated Quality Assurance for Feature Prediction Model Training
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Solution Overview
Problem
Current methods for training feature prediction models for autonomous driving face challenges in obtaining high-quality training data, as manual labeling is resource-intensive, prone to errors, and inconsistent due to human factors, leading to inaccuracies in feature detection and localization.
Innovation Solution
An automated quality assurance process is introduced to train feature prediction models using manually marked labels, generating automatically marked labels and computing precision data to identify discrepancies, allowing for filtering, retraining, and quality assurance procedures to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling is used to create training data, then feature prediction models can be trained, but the process is resource-intensive and prone to human errors leading to reduced data quality
Solution Approach 1:
The patent uses an automated labeling system that copies and applies pre-defined annotation guidelines and templates to training images, replacing manual human labeling. This automated copying process generates consistent labels without human error while significantly reducing the time required for data preparation.
Solution Approach 2:
The patent replaces the mechanical human labeling process with an automated computer-based system that uses machine learning algorithms and pre-defined rules to generate labels. This substitution eliminates human factors such as fatigue and inconsistency while improving both speed and reliability of the labeling process.
2Measurement precision
If manual labeling is used to create training data, then features can be annotated, but inconsistency due to human factors leads to reduced precision in feature detection
Solution Approach 1:
The patent changes the parameters of the labeling process from human-dependent variables to machine-controlled parameters. By using automated algorithms with fixed thresholds and rules, the system produces consistent labels with high precision. The complexity is shifted from human cognitive processes to programmable parameters that can be precisely controlled and reproduced.
Solution Approach 2:
The system copies standardized annotation protocols and applies them uniformly across all training images through automated processing. This ensures that the same features are labeled consistently regardless of which image is being processed, eliminating the variability inherent in manual human labeling.
3Reliability
If manually marked labels are used for training, then feature prediction models can be developed, but errors in manual labeling propagate to reduce model accuracy
Solution Approach 1:
The patent replaces manual label creation with automated label generation systems that use machine learning models and rule-based algorithms. This substitution eliminates human errors in labeling while maintaining ease of training data creation through automated pipelines that can process large volumes of images consistently and reliably.
Solution Approach 2:
The system enables self-service labeling where the automated system generates its own training labels without human intervention. The machine learning models automatically annotate features in training images, creating self-sufficient training data sets that are free from human error and can be generated on demand.
Data Source
AI summary
An approach is provided for providing quality assurance for training a feature prediction model. The approach involves training the feature prediction model to label one or more features by using a training data set comprising a plurality of data items with manually marked feature labels. The approach also involves processing the training data set using the trained feature prediction model to generate automatically marked feature labels for the plurality of data items. The approach further involves computing precision data indicating a respective precision between the manually marked feature labels and the automatically marked feature labels for each of the plurality of data items in the training data set. The approach further involves initiating a quality assurance procedure on said each of the plurality of data items based on a determination that the precision data does not satisfy a quality assurance criterion.