Friction Estimation Using Road Surface Classification
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Solution Overview
Problem
Current methods for estimating maximum tire friction are unreliable at low friction forces, especially during straight driving without acceleration or braking, and rely heavily on driving dynamics measurements which are not accurate in real-time, leading to inefficient warning systems and potential safety hazards.
Innovation Solution
A system that combines road surface sensor data with historical friction estimates and driving dynamics measurements to provide a more accurate and reliable estimation of maximum friction, using statistical methods and neural networks to classify and interpolate friction values, while also considering tire aging and changes in road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving dynamics measurement methods are used to estimate maximum friction, then the estimation can be obtained in real-time during braking or cornering, but the measurement is unreliable when friction forces are slight (during straight driving without acceleration or braking)
Solution Approach 1:
The system performs preliminary classification of road surfaces using sensors before friction estimation is needed. Road surface characteristics are categorized in advance, and when actual friction measurement conditions arise, the pre-classified information is combined with current driving dynamics to rapidly determine friction coefficients, enabling reliable estimation even in conditions where traditional methods fail
Solution Approach 2:
Road surface classification results serve as an intermediary between sensor measurements and friction estimation. The classification system translates complex sensor data into simplified road surface categories that can be combined with driving dynamics measurements to produce reliable friction estimates across all driving conditions, including straight driving without acceleration or braking
2Measurement precision
If friction estimation is based on high friction forces (braking or acceleration), then reliable measurement can be obtained, but the system cannot provide estimates during normal cruising or straight driving
Solution Approach 1:
The system pre-classifies road surfaces using sensors during all driving conditions, not just during braking or acceleration. This preliminary classification ensures that friction information is always available, either from direct measurement when high friction forces are applied or from pre-classified road surface data combined with current driving dynamics during normal cruising
Solution Approach 2:
The system is designed to provide friction estimation under all driving conditions by combining multiple approaches: direct measurement during braking/cornering and sensor-based classification combined with driving dynamics during normal cruising. This multi-functional approach ensures continuous availability of friction information regardless of vehicle operation state
3Adaptability or versatility
If road surface sensors are used to classify road surfaces, then friction information can be obtained during straight driving, but the classification results alone are insufficient for accurate friction estimation
Solution Approach 1:
The system merges road surface sensor classification results with driving dynamics measurements to achieve accurate friction estimation. The classification provides road surface characteristics while driving dynamics provide actual tire-road interaction data, and their combination yields precise friction coefficients that neither method could provide alone
Solution Approach 2:
The system uses feedback from driving dynamics measurements to refine and update the road surface classification. Actual friction measurements during braking or acceleration provide feedback that validates and adjusts the sensor-based classification, improving the accuracy of friction estimation over time and across different road conditions
Data Source
AI summary
A method and apparatus for the estimation of maximum friction between a vehicular tire and a road surface via driving dynamics measurements and at least one sensor measuring the road surface, comprisingmeasuring road surface properties via a sensor;measuring the state of motion of a vehicle and a tire and concluding therefrom a momentary maximum friction coefficient when the tire is subjected to a sufficient friction force;storing a maximum friction estimate concluded from the above-mentioned measurements, along with measuring results regarding road surface properties measured at the moment of measurement;when the tire is not subjected to a friction force sufficient for measuring maximum friction, using, as a maximum friction estimate, the previously measured maximum friction estimate, such that an applied selection criterion for the maximum friction estimate is the newness as up-to-date as possible of the measuring result and a consistency of the road surface measuring result.