Wheel Alignment Detection via Self-Aligned Torque Regression
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Solution Overview
Problem
Current methods for detecting wheel alignment conditions in vehicles are inefficient, relying on manual measurements and comparisons of distances and angles, which can be inaccurate and time-consuming, and do not effectively monitor shifts in wheel alignment as a vehicle is driven.
Innovation Solution
A method and apparatus that utilize a regression model, specifically a multivariate nonlinear regression model or neural network, to predict the self-aligning torque parameter based on vehicle sensor data such as steering wheel angle, speed, lateral acceleration, and power steering torque, comparing measured and predicted values to determine proper wheel alignment, and outputting conditions like negative camber, positive camber, toe-in, toe-out, cross-toe, and total-toe conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurements and comparisons of distances and angles are used to detect wheel alignment conditions, then the detection process can be performed with simple equipment, but the detection accuracy and efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated detection system that uses sensors to measure vehicle parameters (steering wheel angle, speed, lateral acceleration, power steering torque) and a regression model to predict self-aligning torque. This substitution of mechanical measurement with electronic sensing and computational analysis resolves the contradiction by providing high measurement precision through automated data collection and processing, while the complexity is managed through software-based regression modeling rather than complex mechanical measurement apparatus.
2Productivity
If manual methods are used for wheel alignment detection, then the system complexity remains low, but the detection time and productivity worsen
Solution Approach 1:
The detection system operates autonomously by automatically collecting vehicle parameter data through sensors, processing the data through a regression model to predict self-aligning torque, and comparing the predicted value with measured values to determine wheel alignment conditions. This self-service automation eliminates the need for manual measurement and analysis, dramatically improving detection speed and productivity while managing system complexity through integrated software processing.
Solution Approach 2:
The system continuously monitors vehicle parameters and provides real-time feedback on wheel alignment conditions by comparing measured self-aligning torque with predicted values. This feedback mechanism enables immediate detection of alignment deviations and allows for continuous monitoring as the vehicle is driven, improving productivity through automated real-time assessment rather than periodic manual checks.
3Reliability
If traditional measurement methods are used, then the equipment required is simple, but the ability to monitor shifts in wheel alignment during driving deteriorates
Solution Approach 1:
The detection system enables continuous monitoring of wheel alignment conditions during vehicle operation by continuously collecting data from sensors (steering wheel angle, speed, lateral acceleration, power steering torque) and continuously processing this data through the regression model. This continuous action allows the system to detect shifts in wheel alignment as they occur during driving, providing reliable real-time monitoring rather than intermittent manual measurements.
Data Source
AI summary
A method and apparatus that detect wheel misalignment are provided. The method includes predicting a self-aligning torque parameter based on a regression model determined from a dataset including one or more from among a steering wheel angle parameter, a speed parameter, a torsion bar torque parameter, a lateral acceleration parameter, and a power steering torque parameter, comparing a measured self-aligning torque parameter and the predicted self-aligning torque parameter, and outputting a wheel alignment condition indicating whether the wheel alignment is proper if the self-aligning torque parameter and the predicted self-aligning torque parameter are within a predetermined value based on the comparing.