Customized Driving Event Prediction Model Using Feedback Labels
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Solution Overview
Problem
Current video systems face challenges in generating accurate driving event classifications due to varying user interpretations and insufficient data for specific customer scenarios, leading to resource-intensive and erroneous machine learning model training.
Innovation Solution
A video system that processes customer-specific video data using a feature extraction model, embedding layer, classifier machine learning model, and customizer machine learning model to generate customized predictions based on general and user feedback labels, optimizing model weights and training models to align with customer-specific requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using general labels without customer-specific feedback, then training resources are conserved, but classification accuracy and reliability deteriorate due to varying user interpretations
Solution Approach 1:
The system performs preliminary training with general labels to establish a baseline model, then applies fine-tuning with customer-specific feedback labels. This two-stage approach ensures the model is pre-prepared with general knowledge before being customized, reducing the need for extensive retraining while improving accuracy for specific customers.
Solution Approach 2:
The system incorporates customer feedback labels (corrections and confirmations) as supervisory signals to fine-tune the pre-trained model. This feedback mechanism allows the model to adapt to customer-specific interpretations of driving events, improving classification reliability without requiring complete retraining from scratch.
2Reliability
If customer-specific models are trained for each user, then classification accuracy improves, but device complexity and resource consumption increase
Solution Approach 1:
The training process is segmented into two independent stages: (1) general pre-training on diverse driving event data, and (2) customer-specific fine-tuning on labeled customer feedback. This segmentation allows the complex task of creating customized models to be broken down into manageable steps, reducing overall system complexity.
Solution Approach 2:
Instead of training entirely new models for each customer, the system creates customized versions by fine-tuning a shared pre-trained model. This copying approach with selective adaptation reduces complexity by reusing the base model architecture and weights, requiring only incremental adjustments based on customer feedback.
3Measurement precision
If insufficient customer data is available, then data collection requirements are reduced, but model training accuracy deteriorates due to inadequate labels
Solution Approach 1:
The system performs preliminary training with general labels to establish a baseline model before customer-specific fine-tuning. This preliminary action ensures that even with limited customer data, the model starts with pre-acquired knowledge, reducing the minimum data volume required for effective customization while maintaining label accuracy.
Solution Approach 2:
The system changes the training parameters by using a pre-trained model as the starting point for fine-tuning. This parameter change (initialization state) allows the model to achieve better accuracy with fewer customer labels, as the pre-trained weights provide a strong foundation that requires minimal adjustment.
4Reliability
If error correction through feedback labeling is performed, then classification reliability improves, but loss of time increases due to manual review processes
Solution Approach 1:
The system performs preliminary classification using a pre-trained model before presenting candidates for feedback labeling. This preliminary action filters out high-confidence predictions that don't require review, reducing the time loss associated with manual feedback while maintaining reliability through targeted error correction on uncertain cases.
Solution Approach 2:
The system applies feedback labeling selectively to only those predictions where the model expresses uncertainty or where customer-specific nuances are likely to matter. This partial action approach avoids the time loss of reviewing all predictions while still improving reliability through targeted feedback on critical cases.
Data Source
AI summary
A device may process video data, with a feature extraction model, to generate features, and may process a customer identifier, with an embedding layer, to generate an input. The device may optimize model weights for a classifier model and a customizer model, and may process the features, with the classifier model, to generate general predictions. The device may process the features, the input, and the general predictions, with the customizer model, to generate customer predictions, and may calculate first errors for the general predictions. The device may calculate second errors for the customer predictions, and may train the classifier model and the feature extraction model with the first errors and the optimized model weights. The device may train the customizer model and the embedding layer with the second errors and the optimized model weights. The device may implement the trained classifier model, feature extraction model, customizer model, and embedding layer.