A system and method are provided for estimating and applying vehicle tire traction. Vehicle data (e.g., movement and location-based data) and tire sensor data are collected at a vehicle and transmitted to a remote computing system (e.g., cloud server). A wear status is determined, and traction characteristics determined for at least one tire, based at least on the vehicle data and the determined tire wear status. The predicted tire traction characteristics are transmitted from the remote computing system to an active safety unit associated with the vehicle, or a fleet management system, wherein the recipient is configured to modify vehicle operation settings based on at least the predicted tire traction characteristics. A maximum speed for the vehicle may be defined by the recipient, or a minimum following distance where, e.g., the vehicle is one of multiple vehicles in a defined platoon.

An abnormality detecting apparatus includes a signal obtaining unit, a first index calculation unit, a spectrum calculation unit, a standardization unit, and a second index calculation unit. The signal obtaining unit obtain signals indicating a rotation speed of the wheel assembly, as pulses having a rise. The first index calculation unit calculates a first index indicating a temporal variation of the rise of each of the pulses. The spectrum calculation unit calculates a frequency spectrum of rotational orders from a first order to an mth order of the first index calculated for each of the pulses. The standardization unit standardizes a gain for each rotational order of the frequency spectrum using a mean value and a standard deviation of the gain for each rotational order when there is no abnormality. The second index calculation unit calculates a second index for determining whether there is an abnormality.

Disclosed herein is an indirect tire pressure monitoring apparatus including a wheel speed sensor installed in each wheel to detect a wheel speed, and a controller electrically connected to the wheel speed sensor, wherein the controller determines a decompression determination indicator according to the wheel speed detected by the wheel speed sensor, determines a turning compensation amount for turning compensation of the decompression determination indicator, compensates the decompression determination indicator based on the turning compensation amount, and determines whether the tire is decompressed based on the compensated decompression determination indicator.

A method for checking and/or monitoring the use of a tire (1) mounted on a vehicle (7), comprises the following steps: acquisition, by a microphone (10) arranged inside the tire, of an acoustic response of said tire (1) obtained under the effect of a pulsed mechanical stress thereon, and processing of said response in the frequency domain, characterized in that said processing identifies, in the response in the frequency domain, two spectrum spikes situated on either side of a reference frequency corresponding to the first cavity mode of the tire and, as a function of the frequency deviation between the two duly identified spikes, determines information relating to at least one parameter of use of the tire.

A tire pressure monitoring device of an electric vehicle includes a wheel speed sensor sensing a speed of each wheel of the electric vehicle, and a controller selectively calculating an intercept value of ΔFR and a cumulative average value of the ΔFR based on a difference between a torque of a front wheel motor and a torque of a rear wheel motor, calculating an intercept value of ΔLR and an intercept value of ΔDiag based on the speed of each wheel, and monitoring a tire pressure of each wheel based on the calculated intercept value of the ΔFR or the cumulative average value of the ΔFR, the calculated intercept value of the ΔLR, and the calculated intercept value of the ΔDiag.

A tire side device is provided with a vibration detection unit that outputs a detection signal corresponding to the magnitude of tire vibration, a control unit that performs to generate road surface data indicating a road surface condition that appears in the waveform of the detection signal, and a first data communication unit that transmits the road surface data. Furthermore, a vehicle body side system is provided with a second data communication unit that receives the road surface data transmitted from the first data communication unit, and a road surface determination unit that determines the condition of the road surface that the vehicle is traveling on the basis of the road surface data. In addition, sensing is performed by the control unit under different sensing conditions at the tire side device of at least one tire among a plurality of tires and the tire side device of at least one other tire among the plurality of tires, and road surface data generated on the basis of the different sensing conditions is transmitted from the first data communication unit.

A method for determining an item of fastening information of a wheel of a motor vehicle, wherein rotational movements of the wheel are detected by at least one sensor. The sensor provides a wheel speed signal to an electronic control unit, which determines the fastening information from the wheel speed signal. A first time-frequency transformation of the wheel speed signal is carried out, whereby a first transformation signal is generated, after which a second time-frequency transformation of the first transformation signal is carried out. A second transformation signal is generated, in particular after which a first fastening parameter is obtained from the second transformation signal, after which the fastening information is calculated/determined as a function of the second transformation signal and/or the first fastening parameter.

A vehicle control apparatus may include an information acquisition device configured to acquire information related to at least one motor in a vehicle and information related to a plurality of wheel speed sensors; a calculator configured to determine an estimated wheel speed value according to the information related to the at least one motor and the information related to the wheel speed sensors; and a controller configured to determine an air pressure state of a tire corresponding to each of the wheel speed sensors of the vehicle according to the estimated wheel speed value and the information related to the wheel speed sensors.

A system and method are provided for Bayesian updating of distributions of factors that affect tire wear. Information is accumulated in data storage regarding probability distributions corresponding to each of a respective plurality of tire wear factors. Vehicle data comprising movement data and location data collected in association with a vehicle is transmitted from the vehicle to a centralized (e.g., cloud) computing device or network. At least one observation corresponding to one or more of the plurality of factors is generated based on the transmitted vehicle data. A Bayesian estimation is then generated of a tire wear status at a given time for at least one tire associated with the vehicle, based at least on the at least one generated observation and the stored information regarding probability distributions. The predictions accordingly carry a measure of uncertainty, and Bayesian inference can be used to update distributions based on the observations.

A system and method are provided for predicting the non-linear progression in vehicle tire wear. The system determines an original tread depth for a tire associated with a vehicle, and further determines an initial wear rate for the tire based at least partially on the original tread depth. One or more tire conditions are measured as time-series inputs to a predictive tire wear model, for example utilizing “brush-type” tire wear models for a contact interface between a base material of the tire and road surfaces, wherein the interface is represented as a plurality of independently deformable elements, wherein a current wear rate is normalized based at least partially on said inputs to the initial wear rate for the tire. The system can then predict a tire wear status of the tire for specified future parameters, such as for example a specified future distance traveled or a time traveled.