Wheel Alignment Detection Using Sensor Data Classification
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Solution Overview
Problem
Current methods for detecting wheel alignment conditions in vehicles are inefficient, relying on manual measurements and lacking real-time, accurate diagnostics for various alignment issues such as camber, toe, and caster angles.
Innovation Solution
A method and apparatus that utilize vehicle sensor data, including steering wheel angle, speed, lateral acceleration, and torque parameters, to normalize and analyze datasets through a classification model, determining whether the wheel alignment is within a predetermined range, and outputting results on a display or server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual measurement methods are used for wheel alignment detection, then device complexity is reduced, but measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated detection system that uses vehicle sensors (steering angle sensor, speed sensor, acceleration sensor, torque sensor) to collect data and a machine learning classification model to analyze the data and determine wheel alignment conditions, thereby improving measurement precision while maintaining manageable system complexity
Solution Approach 2:
The patent introduces a classification model as an intermediary between raw sensor data and wheel alignment determination. The model processes normalized sensor data through training and validation to accurately classify wheel alignment conditions, serving as a mediator that translates complex sensor readings into meaningful alignment diagnostics
2Productivity
If real-time sensor data analysis is implemented, then detection speed and productivity are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by collecting and normalizing sensor data continuously during vehicle operation, preparing the data in advance for analysis. The system normalizes multiple sensor parameters (steering angle, speed, acceleration, torque) before feeding them into the classification model, enabling real-time detection without last-minute processing complexity
Solution Approach 2:
The patent transforms raw sensor parameters into normalized parameters through data normalization processes. By changing the parameter representation from raw sensor values to normalized values, the system enables the classification model to process data efficiently in real-time while maintaining detection accuracy
3Measurement precision
If multiple sensor parameters are collected and analyzed, then measurement precision and diagnostic accuracy are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a classification model that serves multiple functions: it processes various sensor parameters (steering angle, speed, acceleration, torque), handles data normalization, performs classification, and determines different wheel alignment conditions (camber, toe, caster). This multi-functional approach improves diagnostic accuracy while managing system complexity through a unified processing framework
Solution Approach 2:
The patent merges multiple sensor data streams (steering angle sensor, speed sensor, acceleration sensor, torque sensor) into a unified dataset that is normalized and processed together by the classification model. By combining these parameters into a single analysis pipeline, the system achieves comprehensive alignment detection without proportionally increasing complexity
Data Source
AI summary
An apparatus and method that detect a wheel alignment condition are provided. The method includes receiving a dataset comprising one or more from among a steering wheel angle parameter, a speed parameter, a lateral acceleration parameter, a self-aligning torque parameter and a power steering torque parameter, normalizing the received dataset, analyzing the normalized dataset according to a model for determining a wheel alignment condition, and outputting a value indicating whether the wheel alignment is within a predetermined value based on the model.