Motor Vehicle Control Calibration With Predictive Driver Deviation
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Solution Overview
Problem
Current motor vehicle control systems lack the ability to adapt to individual driving behaviors, leading to suboptimal performance and reduced driver acceptance in assistance systems.
Innovation Solution
A control system incorporating a predictive model trained to reflect deviations in driving behavior, which adjusts control operations by combining a conventional controller's output with a second output variable from the predictive model, allowing for personalized adaptation to individual drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a uniform calibration of the control system is utilized, then the system is simple to implement and maintain, but it cannot adapt to individual driving behaviors of different drivers
Solution Approach 1:
The control system is segmented into two independent components: a base controller with uniform calibration and a predictive model for individual adaptation. This segmentation allows the system to maintain simplicity in the base controller while adding adaptability through the separate predictive model that processes driver behavior data independently.
Solution Approach 2:
The predictive model serves multiple functions: it captures individual driving behavior patterns, predicts driver intentions, and adjusts control operations accordingly. This multi-functionality enables a single added component to provide comprehensive adaptability without requiring multiple separate systems.
2Ease of operation
If the control system is adapted to individual driving behavior using a predictive model, then driver acceptance and effectiveness improve, but the device complexity increases
Solution Approach 1:
The predictive model automatically learns and adapts to individual driver behavior patterns without requiring manual configuration or intervention. The system observes driver actions, builds predictive models autonomously, and applies adaptations automatically, making the complexity management self-service rather than requiring user setup.
Solution Approach 2:
The system implements continuous feedback loops where the predictive model monitors actual driver behavior, compares it with predicted behavior, and refines its predictions accordingly. This feedback mechanism enables the system to self-optimize and improve driver acceptance over time without manual reconfiguration.
3Adaptability or versatility
If a predictive model is trained to reflect deviation of driving behavior, then the control system becomes personalized, but the training process and data requirements increase complexity
Solution Approach 1:
The predictive model is trained in advance during a calibration phase before actual use. During this preliminary action phase, the system collects and processes driver behavior data to establish baseline patterns. Once trained, the model requires minimal additional data processing during normal operation, as the heavy lifting of pattern recognition has already been completed.
Solution Approach 2:
The system collects more driver behavior data than strictly necessary for basic functionality during the training phase. This excessive data collection ensures comprehensive coverage of various driving scenarios and conditions, making the predictive model more robust and reducing the need for retraining in different situations.
Data Source
AI summary
A control system for a motor vehicle, for outputting a controlled variable, with the aid of which a directly controlled variable of a motor vehicle is adjustable via suitable control operations, in order to adapt the directly controlled variable to a reference variable of the control system. The control system includes a controller, which is configured to output a first output variable on the basis of the directly controlled variable of the motor vehicle, and on the basis of the reference variable of the control system. The control system further includes a predictive model, which may be trained to output a second output variable that reflects a deviation of a driving behavior of a driver of the motor vehicle from the first output variable of the controller. The controlled variable of the control system encompasses an addition of the first output variable and the second output variable.