Vehicle ML Model Update via Part Replacement Detection
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Solution Overview
Problem
Machine learning models used in vehicles experience a decline in prediction precision when vehicle parts are replaced, as they are tailored to the original part properties.
Innovation Solution
A machine learning device and system that detects vehicle part replacements, acquires identification information, and updates the machine learning model by receiving a new model trained on the replaced part's data sets from a server, ensuring the model remains accurate post-replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained specifically for a particular vehicle part configuration, then prediction precision is improved, but the model becomes invalid when the vehicle part is replaced
Solution Approach 1:
The patent implements a dynamic model update mechanism where the machine learning model is automatically updated when vehicle part replacement is detected. The system monitors part status, detects replacements through identification information comparison, and triggers model updates to adapt to new part configurations, making the system dynamically adaptable rather than static.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors vehicle part status and uses this information to determine when model updates are needed. By comparing identification information before and after replacement, the system receives feedback about part changes and accordingly updates the machine learning model to maintain prediction accuracy.
2Measurement precision
If the machine learning model is updated every time a vehicle part is replaced, then prediction precision is maintained, but system complexity and update frequency increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple machine learning models corresponding to different vehicle part configurations before deployment. When a part replacement is detected, the system can immediately switch to or activate the pre-prepared model suitable for the new configuration, avoiding the need for complex real-time model retraining or updating processes.
3Reliability
If identification information is transmitted to the server for model updates, then the correct new model is received, but communication overhead and update time increase
Solution Approach 1:
The patent implements a universal model storage architecture at the server where multiple machine learning models for different vehicle part configurations are stored in a unified manner. The server can retrieve and transmit the appropriate model based on the vehicle identification information and part configuration, making the server capable of serving multiple vehicle types and part configurations through a single system.
Data Source
AI summary
The machine learning device includes a predicting part configured to use a machine learning model to predict predetermined information, an updating part configured to update the machine learning model, and a part information acquiring part configured to detect replacement of a vehicle part and acquire identification information of the vehicle part after replacement. The updating part is configured to receive a new machine learning model trained using training data sets corresponding to the vehicle part after replacement from a server and apply the new machine learning model to the vehicle, if a vehicle part relating to input data of the machine learning model is replaced with a vehicle part of a different configuration.