Method for estimating the probability of uneven tire wear

A machine learning-based method estimates uneven tire wear using vehicle driving parameters, addressing the inefficiencies of current methods and enhancing safety and tire longevity.

DE102024211306A1Pending Publication Date: 2026-05-28CONTINENTAL REIFEN DEUTSCHLAND GMBH
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Patent Information

Application Number
DE102024211306
Authority / Receiving Office
DE · DE
Patent Type
Applications
Current Assignee / Owner
Filing Date
2024-11-26
Publication Date
2026-05-28

AI Technical Summary

Technical Problem

Existing methods for assessing tire wear fail to provide timely and efficient information about uneven tire wear, which can lead to safety issues and reduced tire lifespan, requiring costly and time-consuming manual inspections by trained personnel.

Method used

A data-driven approach using machine learning to estimate the probability and extent of uneven tire wear based on vehicle driving parameters, utilizing existing vehicle sensors and low-frequency telemetry data for training and prediction.

Benefits of technology

Provides a cost-effective and efficient method to detect uneven tire wear, reducing the need for additional sensors and manual inspections, while improving driving safety and extending tire lifespan.

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Abstract

The invention relates to a method for estimating the probability of uneven tire wear on the tires of a vehicle, comprising the following steps: a) driving the vehicle during a recording period, wherein a plurality of driving parameters are recorded during the recording period, one or more of the driving parameters being derived from time-dependent position data of the vehicle, the derived driving parameters including at least information about the distance traveled by the vehicle during the recording period, and b) estimating the probability of uneven tire wear during the recording period for one or more tires of the vehicle, using an electronic data processing device, the electronic data processing device comprising a storage unit.wherein a machine learning-based estimation module is stored on the storage unit, by means of which the probability of uneven tire wear occurring during the recording period is estimated.
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