AI-Driven ECU Control via Phased Data Training
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Solution Overview
Problem
Existing electronic control units (ECUs) in motor vehicles, especially those using artificial intelligence for advanced driver assistance systems, face challenges in training AI algorithms in real traffic scenarios and optimizing control functions, with deep learning methods leaving room for improvement and difficulties in replacing existing ECUs with AI-based ones.
Innovation Solution
A method involving acquiring and caching operating data to create labeled training data, which is used to train AI through supervised learning, optimizing ECUs in a simulation phase, and activating/deactivating control functions using binary control signals in a prediction phase, potentially utilizing recurrent neural networks and integrated environments like hardware-in-the-loop simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning methods are used to train AI algorithms in ECUs, then intelligent behavior and control functions are improved, but the complexity of training and deployment in real traffic scenarios increases
Solution Approach 1:
The patent segments the training process into distinct phases: acquisition phase for collecting operating data, simulation phase for generating labeled training data, and training phase for deploying AI models. This segmentation allows each phase to be optimized independently, reducing overall training complexity while maintaining intelligent behavior capabilities.
Solution Approach 2:
The patent performs preliminary actions by collecting and archiving operating data during normal vehicle operation before actual AI training is needed. This pre-acquisition of data during the acquisition phase reduces the complexity of real-time training by having ready-to-use training datasets when deployment is required.
2Manufacturing precision
If AI algorithms are trained using archived operating data, then control function optimization is improved, but the time required for data acquisition and processing increases
Solution Approach 1:
The patent implements continuous data acquisition during normal vehicle operation through the acquisition phase, where operating data is collected and archived continuously without interrupting vehicle functionality. This continuous collection process eliminates the need for separate, time-consuming data gathering sessions, allowing control function optimization to proceed with readily available data.
3Adaptability or versatility
If existing ECUs are replaced with AI-based ECUs, then intelligent control capabilities are improved, but the reliability and safety of control functions may be compromised
Solution Approach 1:
The patent introduces a simulation environment as an intermediary between existing ECUs and AI-based ECUs. During the simulation phase, the system generates labeled training data that mirrors real operating conditions, allowing AI models to be trained and validated in a controlled environment before deployment. This intermediary training process ensures reliability by verifying AI performance against known scenarios before full integration.
4Quantity of substance
If operating data are cached in cloud platforms, then data storage and processing capabilities are improved, but wireless data transmission requirements and system complexity increase
Solution Approach 1:
The patent creates a multi-functional system where the data archiving mechanism serves multiple purposes: storing operating data for historical analysis, providing training datasets for AI development, and enabling retrospective optimization of control functions. This universal data repository reduces the need for separate specialized systems, thereby reducing overall system complexity despite increased storage requirements.
Data Source
AI summary
A method for operating a motor vehicle includes acquiring current operating data in order to obtain archived operating data during an acquisition phase, evaluating the archived operational data in order to obtain labeled training data for an artificial intelligence during a simulation phase, training the artificial intelligence with the labeled training data during a training phase, and activating or deactivating an ECU or a control function (A, X) of the ECU of the motor vehicle by means of the artificial intelligence during a prediction phase.