Bridgestone Europe Nv/sa

Alessandro Boldrini

Lorenzo Alleva

Marco Pascucci

Marco Andrea Maggi

Vincenzo Ciaravola

Valerio Bortolotto

Alfredo Corollaro

Vittorio Nicolosi

Rufini Flavia

Alessandra Magliozzi

Manfredi Massimilla

Alberto Nantuono

A system and a method for estimating the International Roughness Index (IRI) of a road or road segment includes determining the IRI as a function of physical quantities relating to the motion of a vehicle, for instance the vertical accelerations, and to the vehicle itself, for instance the damping and stiffness coefficients of the suspensions of the vehicle and the tires mounted on the vehicle.

Method and related system for estimating the International Roughness Index of a road segment

The invention concerns a method and a system for recognizing the presence of any irregularities of any road pavement.

Method and system for the recognition of the irregularities of a road pavement

The invention concerns a tread wear monitoring method comprising a tread wear model calibration step () and a tread wear monitoring step (), wherein the tread wear model calibration step () includes determining () a calibrated tread wear model based on tread-wear-related quantities and first frictional-energy-related quantities. The tread wear monitoring step () includes: acquiring (), from a vehicle bus () of a motor vehicle () equipped with two or more wheels fitted, each, with a tire, driving-related quantities related to driving of the motor vehicle (); computing () second frictional-energy-related quantities related to frictional energy experienced, during driving, by a given tire of the motor vehicle () by providing a predefined vehicle dynamics model that mathematically relates the acquired driving-related quantities to the second frictional-energy-related quantities, and computing the second frictional-energy-related quantities by inputting the acquired driving-related quantities into the predefined vehicle dynamics model; and performing a tread wear estimation () and a remaining tread material prediction () based on the second frictional-energy-related quantities computed and the calibrated tread wear model. The tread wear model calibration step () further includes: performing () tread wear tests on one or more tires; and measuring () tread-wear-related quantities indicative of tread depth reductions resulting from the performed tread wear tests, and first frictional-energy-related quantities related to frictional energy which the tested tire(s) is/are subject to during the performed tread wear tests. Determining () a calibrated tread wear model includes: providing a predefined reference tread wear model that mathematically relates frictional energy experienced by a tire along a driving route to tread wear caused by said frictional energy through given parameters; computing calibration values of the given parameters by inputting the measured tread-wear-related and first frictional-energy-related quantities into the predefined reference tread wear model; and determining the calibrated tread wear model by applying the computed calibration values in the predefined reference tread wear model.

Tread wear monitoring system and method

The invention concerns a tread wear monitoring method comprising a preliminary step () and a tread wear monitoring step (). The preliminary step () includes: performing tread wear tests on one or more tires; measuring tread-wear-related quantities and first frictional-energy-related quantities, wherein the tread wear-related quantities are indicative of tread wear resulting from the performed tread wear tests, and the first frictional-energy-related quantities are related to frictional energy which the tested tire(s) is/are subject to during the performed tread wear tests; and determining a calibrated tread wear model based on the measured tread-wear-related and first frictional-energy-related quantities. The tread wear monitoring step () includes: acquiring, from a vehicle bus () of a motor vehicle () equipped with two or more wheels fitted, each, with a tire, driving-related quantities related to driving of the motor vehicle (); computing, based on the acquired driving-related quantities and a predefined vehicle dynamics model related to the motor vehicle (), second frictional-energy-related quantities related to frictional energy experienced, during driving, by at least one tire of the motor vehicle (); estimating, based on the second frictional-energy-related quantities and the calibrated tread wear model, tread wear experienced by said at least one tire of the motor vehicle () during driving; and estimating a current average remaining tread material amount of said at least one tire of the motor vehicle () based on the estimated tread wear. Additionally, the preliminary step () further includes: determining, based on one or more of the measured tread-wear-related quantities, a first correction factor related to irregular tread wear due to tire features; and training an artificial neural network to provide second correction factors related to irregular tread wear due to tire usage. Finally, the tread wear monitoring step () further includes: providing a second correction factor by means of the trained artificial neural network based on one or more of the acquired driving-related quantities; and computing a corrected remaining tread material amount based on the current average remaining tread material amount, the first correction factor and the second correction factor provided by the trained artificial neural network based on the one or more acquired driving-related quantities.

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Rome, Italy

Alessandro Boldrini