In-Vehicle Control Model Updates for Real-Time ECU Adaptation
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Solution Overview
Problem
Existing electronic control systems in vehicles face challenges in adapting to real-time changes in vehicle behavior due to the limitations of incremental software updates, which fail to effectively leverage the vast data exchanged between vehicle functional units.
Innovation Solution
The implementation of distributed and federated machine intelligence systems that utilize a model manager, control component, and learning component to construct and modify trainable models based on observational data, allowing for dynamic calibration of vehicle functional units and adaptation to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If incremental software updates are used to maintain vehicle operation, then system reliability is improved, but the system cannot adapt in real-time to changing vehicle behavior
Solution Approach 1:
The patent transforms the static incremental update approach into a dynamic system where machine learning models continuously learn from observational data exchanged between vehicle functional units. The system adapts in real-time by updating models based on changing vehicle behavior patterns, while maintaining reliability through the structured update framework.
Solution Approach 2:
The system implements feedback mechanisms where observational data from vehicle functional units is collected, processed through machine learning models, and used to generate updated control parameters. This closed-loop feedback enables continuous adaptation while maintaining system reliability through validated update processes.
2Adaptability or versatility
If machine learning models are trained using vast amounts of exchanged data, then adaptability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing task by distributing machine learning model training across multiple vehicle functional units. Each unit contributes observational data and computes local model updates, reducing the processing burden on any single component while collectively achieving comprehensive adaptability.
Solution Approach 2:
The system creates a universal machine learning framework that can process diverse observational data from various vehicle functional units through a common model structure. This multi-functional approach handles different data types and sources uniformly, reducing overall system complexity.
Data Source
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AI summary
Systems, devices, computer-implemented methods, and/or computer program products that facilitate modifying electronic control system behavior using distributed and/or federated machine intelligence. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components can comprise a model manager, a control component, and a learning component. The model manager can construct a trainable model using a pre-trained template model that is received via an in-vehicle network from a domain chief. The control component can dynamically vary a control parameter of a vehicle functional unit using the pre-trained template model to calibrate an output of the vehicle functional unit. The learning component can modify the trainable model based on observational data of the vehicle functional unit.