Vehicle Tire Wear Mapping Using ML-Extended Road Data
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Solution Overview
Problem
Existing methods for determining tire wear in vehicles are time-consuming, especially for fleets or shared vehicles, and often violate data protection regulations due to the need for large data transmissions.
Innovation Solution
A method using classified and extended map data, where the link between a vehicle's position and tire wear is established through test drives and extrapolated using machine learning to areas without test drive data, allowing for accurate tire wear monitoring without extensive data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of tire tread depth is performed, then measurement precision is achieved, but productivity deteriorates due to time-consuming inspection especially for fleets or shared vehicles
Solution Approach 1:
The system enables self-service monitoring where the tire wear assessment is performed automatically without human intervention. The server autonomously processes position data, compares it with map data, and determines tire wear indicators, eliminating the need for manual inspection while maintaining measurement precision.
Solution Approach 2:
The patent replaces the mechanical manual inspection process with an automated information processing system. Instead of physically measuring tread depth with gauges, the system uses position data processing and map data comparison to determine tire wear, substituting mechanical measurement with computational analysis.
2Productivity
If automated monitoring with precise position tracking is implemented, then productivity is improved, but loss of information increases due to data protection restrictions on transmitting large amounts of position data
Solution Approach 1:
The system extracts only the essential information needed for tire wear determination from the position data. Instead of transmitting and processing complete high-precision position information, it uses coarse position data to identify geographic areas, extracting only the necessary spatial context for wear assessment while leaving detailed position information on the client device.
Solution Approach 2:
The patent applies different levels of position data precision to different functional requirements. Coarse position data is used for area identification and map matching, while no detailed position data is transmitted. This local differentiation of data quality allows automated monitoring to function while respecting data protection constraints.
3Loss of information
If coarse position determination is used to protect data, then data protection is improved, but measurement precision deteriorates due to reduced position accuracy
Solution Approach 1:
The patent introduces map data as an intermediary between position information and tire wear determination. The coarse position data identifies a geographic area in the map data, which then provides the context for assessing tire wear based on road conditions, terrain, and other factors associated with that area. This intermediary allows accurate wear determination without requiring precise position tracking.
Solution Approach 2:
The system transitions from relying on precise spatial coordinates (one dimension of position accuracy) to using geographic area identification combined with map data attributes (adding the dimension of contextual information). This dimensional shift allows tire wear determination to be based on the characteristics of the traveled area rather than exact position measurements.
Data Source
Figure 1~2
AI summary
A method for determining tire wear of a vehicle, comprising - providing classified map data, wherein the classified map data contains a link, determined by test drives, between a position of the vehicle and a characteristic value for the tire wear; - providing extended map data by transferring the link to positions at which no test drives were performed using machine learning, and - monitoring a position of the vehicle and determining a characteristic value for the tire wear from the position of the vehicle, the classified map data, and the extended map data.