Tire Tread Wear Prediction

Estimating tire wear using AI models to detect uneven wear at an early stage. The method involves capturing images of a tire's tread from the front, inputting them into trained machine learning models, and deriving outputs indicating the degree of uneven wear. One model estimates overall uneven wear, while another estimates groove depth. By combining the outputs, it determines if the tire needs replacement. The models are trained using labeled images of actual tires at various wear levels.

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Tire sensing system to accurately measure tire wear, tread depth, and monitor tire condition in real-time and over extended durations. The system uses millimeter-wave radar sensors mounted on the tire to image the tire surface and grooves. By detecting reflected radar signals, it can accurately measure tire dimensions like radial extents, even in the presence of debris. This allows determining tread depth, wear patterns, and detecting foreign objects in the tread. The radar sensors can be integrated into the tire itself or attached externally. The system provides reliable, direct measurement of tire conditions without requiring internal sensors or embedding devices in the tire.

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Method for predicting the remaining life of a tire on an aircraft before replacement. The method involves tracking the wear of a specific wear indicator in the tread of the tire. The wear indicator is a feature with a known shape and location in the tread. By monitoring the appearance of the wear indicator as the tire wears, the remaining life can be estimated. This allows anticipating when the tire needs to be replaced without relying solely on direct visual inspection of the tread height. The wear indicator provides a more reliable and predictive measure of wear progression.

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Modeling and predicting tire performance and providing feedback based on summarized vehicle data to estimate tire wear, traction, and tread depth in real-time. The method involves extracting relevant features from high-frequency vehicle data using local processing, then transmitting the summarized data to a remote server for estimation. This reduces the volume of data needed for transmission and processing compared to raw data. The server estimates tire wear, traction, and tread depth based on the summarized features.

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Estimating tire wear based on driving patterns to provide more accurate and convenient tire wear monitoring without requiring manual tire inspections. A model learns the correlation between driving patterns and tire wear. It estimates tire wear for a specific driver based on their driving history. This allows tracking tire wear over time and notifying when replacement is needed. It also provides wear info to drivers during driving and to external services like management systems.

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Method for estimating tire wear and predicting tire performance using real-time vehicle data. The method involves continuously collecting vehicle and tire data, determining current tire wear status based on that data, and predicting tire performance characteristics like traction, durability, and fuel efficiency. Feedback is provided to users based on the predictions. This allows estimating tire life from periodic measurements instead of manual tread depth checks. The method uses statistical models, frequency shifts, and feature extraction from sensor data to estimate tire wear.

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Predicting tire wear and traction capabilities in real-time using vehicle and tire data. The method involves estimating tire wear status, predicting tire performance characteristics like traction, and providing feedback based on wear and performance. It leverages sensors, machine learning, and physics models to continuously monitor and analyze tire data to provide actionable insights. The goal is to improve tire management, reduce accidents, and optimize tire performance through real-time feedback and predictive analytics.

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Predicting tire wear and traction capabilities using real-time vehicle and tire data to provide feedback to users. The method involves estimating tire wear status in real-time based on collected vehicle and tire data. Tire performance characteristics like traction are predicted using the wear status. Feedback is provided to users selectively based on the predicted wear and performance. The tire wear estimation leverages techniques like Bayesian estimation, tire wear models, and sensor data.

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Accurately estimating tire wear using multiple predictors to provide a reliable and accurate tire wear estimation system. The system involves attaching a sensor to the tire to generate a predictor, while also storing data for another predictor in a table. These predictors, along with vehicle effects, are fed into a model to generate an estimated tire wear rate. The estimated wear rate is then transmitted to the vehicle's operating system. The multiple predictors provide a more accurate and reliable tire wear estimate compared to single predictor methods.

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A tire wear estimation system that accurately and reliably estimates tire wear state using easily obtained and accurate parameters, and which can operate independently of the vehicle CAN bus. The system involves mounting sensors on the tire to measure footprint length and shoulder length, as well as tire pressure and temperature. This data, along with tire identification, is used to predict tire wear using an analysis module.

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A tire wear prediction method using machine learning to accurately predict tire wear and improve driving safety by finding excessive wear or aging conditions in time. The method involves collecting tire wear data under varying road conditions, vehicle loads, and tire types. This data is used to train a tire wear prediction model that can analyze road factors like quality and curves, as well as vehicle factors like load, to comprehensively predict tire wear. The model can also optimize itself incrementally as new data is added.

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Quantifying tire aging and predicting tire life based on real-time monitoring of tire temperature, pressure, load, speed, and position. The method involves calculating aging units (AU) using Arrhenius reaction rate integration to quantify oxidative aging from contained air temperature and ambient temperature. The AUs accumulate over time and distance traveled. The method predicts tire life state and intervention events for each tire position on a vehicle, and across fleets. It enables real-time tire wear prediction, proactive maintenance, and tire replacement scheduling based on actual operating conditions.

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Estimating tire wear using the effective rolling radius (ERR) to provide more accurate and objective tire wear estimation compared to visual inspection. The method involves tracking ERR using vehicle data, then splitting into two parts based on ERR behavior. When ERR increases, tire wear is estimated using a mileage-based wear curve. When ERR decreases, ERR and contact ratio are converted to references using speed and load, then tire wear is estimated from the linear ERR-wear relationship. This accounts for uneven ERR changes and factors like air pressure.

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Real-time detection of tire wear during vehicle operation using machine learning and historical driving data to provide accurate, real-time tire wear monitoring and hazard warnings. The method involves collecting tire measurement data, historical driving data, and current driving data. It processes this data to obtain wear level, tire warning index, corrected tire warning index, and wear resistance evaluation index. Danger warnings are issued based on the corrected tire warning index, and tires are marked for replacement based on the wear resistance index. The processing involves model training, historical driving analysis, and tire load, environment, and usage data processing.

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Tire wear detection and monitoring system for vehicles that uses real-time data like internal temperature, pressure, and acceleration to accurately detect and display tire wear. The system uses optimization algorithms to improve model accuracy, extracts features like waveform troughs and crests, and trains neural networks to predict tire wear. It displays the wear level of all four tires in real-time using web technology on a display terminal. This enables drivers to see tire condition and take action proactively.

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Estimating tire tread wear using angular velocity sensors to provide a continuous and accurate tread wear monitoring system for vehicles. The method involves comparing the angular velocities of the vehicle's wheels to estimate the differential tread wear between them. This difference is then used to estimate the tread wear of each wheel individually using a calibration relationship. This allows continuous, objective tread wear monitoring without relying on visual inspection.

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Method, device, and equipment for accurately estimating tire wear without additional sensors or devices. The method involves obtaining characteristic parameters of vehicle driving, fuzzifying them using a membership function, mapping fuzzy coefficients based on the membership levels, and calculating tire wear using the fuzzy coefficients. The fuzzification eliminates boundaries between parameter values, making it more accurate to quantify tire wear based on driving characteristics.

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Predicting tire wear and load for vehicles using onboard sensors and machine learning to monitor tire health and prevent premature replacement. The method involves predicting vertical load on a vehicle tire based on measured tire temperature and known thermal characteristics for the tire-vehicle combination. A model generated from tire testing is used to predict tire temperature from input conditions like speed and inflation pressure. This predicted temperature is then used to determine the vertical load. This allows estimating tire wear and replacement needs without direct load sensors. The system can also alert when tread depth falls below thresholds.

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Optimizing heavy vehicle motion management and reducing tire wear by using dynamic tire models to predict tire behavior and wear rates based on vehicle conditions. The models estimate tire parameters like wear, stiffness, rolling resistance, etc. given input like tire data, vehicle state, and environmental factors. By iteratively updating tire models as conditions change, the vehicle control can be optimized to minimize tire wear for specific maneuvers and loads. This involves coordinating motion support devices like brakes and steering based on the tire models. The models also predict stopping distance to optimize braking.

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Method and system for predicting tire wear life to select tires that meet usage requirements. The method involves measuring tire tread depth, width, and calculating wear factors like pattern saturation and width-depth product. Fitting tire wear mileage versus these factors lets predicting tire life for different tires. This helps choosing tires that will wear long enough for intended use.

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Method for managing tire safety of vehicles using IoT to predict tire wear and optimize driving routes to mitigate tire failure risks. The method involves collecting tire service data, analyzing wear rates on different road surfaces, building a tire wear prediction model, determining optimal driving routes based on tire wear, and generating real-time tire parameter alerts. It leverages IoT, machine learning, and blockchain to provide proactive tire management for reducing accidents caused by tire failure.

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Tire damage prediction method for vehicles that improves accuracy by considering vehicle conditions and operating parameters. The method involves obtaining vehicle condition data like road conditions, tire models, and wear levels, along with operating parameters like sensor readings. Damage thresholds are determined based on these conditions and the damage prediction model. Then, tire damage parameters are calculated using the thresholds and operating parameters to predict tire damage probability. This comprehensive approach using vehicle context improves tire damage prediction compared to just sensor data.

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Estimating tire wear without needing specific data for each tire type. The method involves calculating a regression coefficient between slip ratio and driving force based on vehicle data. The tire wear is then estimated using the regression coefficient and a constant that represents complete tire groove wear. This allows estimating tire wear without needing specific slope data for each tire type. The coefficient calculation is done using multiple data sets. Temperature correction and turning radius correction are also included.

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A method and device for predicting tire condition to enable accurate tire wear monitoring and replacement scheduling. The method involves collecting vehicle operating parameters like tire pressure, temperature, and load from sensors. This data is used to establish a tire life prediction model by correlating operating parameters with tire wear. The model is then applied to predict tire life based on ongoing operating conditions. This allows tires to be replaced before catastrophic wear occurs, improving safety compared to fixed replacement intervals.

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A method, device, and computer program for predicting tire life of electric vehicles using big data analysis. The method involves collecting driving, road, and weather data from electric vehicles and maps, processing the data to extract factors affecting tire wear, grouping vehicles by wear levels, and building a tire life prediction model using the grouped wear data. The model can estimate tire life based on current driving conditions and provide warnings to vehicle owners.

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Estimating tire wear and providing feedback to vehicle users about tire performance and replacement times. The method involves collecting vehicle and tire data, determining real-time tire wear status based on that data, and estimating tire performance characteristics like traction and durability based on the wear. Feedback like replacement recommendations and alerts are provided to users based on the estimated performance.

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Method for estimating the state of wear of a tire using vibroacoustic signals recorded while the tire is in contact with the road. The method involves converting the time signals into frequency signals, segmenting them into frequency bands, and predicting the tire wear state using machine learning. Factors like running speed and road conditions are also analyzed using similar techniques.

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Digital tire health estimation for predicting tire wear, fatigue, and damage using machine learning models. The method involves generating tire health models based on input variables like tire type, load, pressure, speed, temperature, etc. These models are iteratively refined over time using historical data. When a tire's input values are measured, the appropriate model is selected and tire health variables like tread depth, carcass health, and ply crack growth are estimated. An output signal indicates the tire's overall health. The method allows predicting tire wear, fatigue, and damage for proactive maintenance and tire management.

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A tire wear estimation system that can accurately estimate tire wear even when missing or unlearned categorical variables are present. The system acquires vehicle and tire data, calculates tire wear using a model with missing value correspondence or non-corresponding computation based on whether unlearned categorical variables are present. This allows estimating tire wear even with incomplete data.

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System for accurately estimating tire wear using machine learning. It involves calculating tire loads using a separate load calculation model, then feeding those loads into a wear calculation model. The wear estimation is compared to actual tire wear measurements to update and refine the wear calculation model over time. This iterative learning process improves the accuracy of estimating tire wear based on load data.

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Estimating tire wear accurately for vehicles like trailers, tractors, and trucks that experience high turning forces. The system uses vehicle data like traveled distance and number of sharp turns to estimate tire wear. It learns a wear calculation model based on measured tire wear and compares it to estimated wear. This allows refining the model over time for improved accuracy.

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Accurately calculating tire wear rate using vehicle data, tire measurements, and machine learning. The method involves obtaining tire, vehicle, and telematics data, and calculating tire wear rate using a self-tuning mathematical model. The model continuously improves by training on tire wear data from multiple vehicles. This allows more accurate tire wear estimation compared to static models.

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Calculating and monitoring a tire wear rate of a vehicle. The computation includes obtaining technical data of at least one tire of a vehicle, obtaining data of the at least one in-operational measurement of at least one property of at least one tire of the vehicle, and calculating a tire wear rate based at least in part on the obtained technical data of the at least one tire of the vehicle, the obtained technical data of the vehicle and the obtained telematics information of the vehicle according to a self-tuning mathematical tire wear model.

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Predicting tire wear life using image processing and machine learning techniques. The method involves collecting images of tire patterns at different wear stages, preprocessing the images, extracting texture features using a co-occurrence matrix and Markov random field model, fusing the features, and using a nearest neighbors classifier to predict tire wear based on the fused features. The technique provides an efficient and accurate way to predict tire wear without manual inspection or specialized equipment, reducing tire blowouts and improving vehicle safety.

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Predicting tire wear on electric vehicles using a T-BOX device to accurately estimate tire wear, monitor tire health, and alert the vehicle owner. The method involves calculating metrics like acceleration duration, acceleration count, and braking duration from vehicle data. These metrics are then used to predict tire wear. The T-BOX device collects the vehicle data, performs the calculations, and provides tire wear estimates to the owner.

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Predicting tire wear performance without actually measuring it, by quantifying and modeling the factors that contribute to tire wear. The method involves calculating a vehicle severity based on lateral acceleration and mileage, calculating route severity from specific acceleration ranges, and combining them to predict tire wear using a wear model. This allows accurate and quick tire wear prediction without the effort and cost of actual measurement as the load changes.

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Estimating tire wear using sensors in the tire and vehicle to improve accuracy and adaptability compared to methods like visual inspection or tread depth gauges. It leverages radial and longitudinal acceleration, pressure, and contact duration from tire sensors to learn tire wear relationships using neural networks. The tire module collects sensor data and transmits it wirelessly to the central vehicle module. The central module estimates tire wear using the transmitted data. The neural network model takes tire pressure, ground contact features, and cycle as input to estimate wear.

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Hierarchical data structure and statistical modeling for predicting tire wear in vehicles. The method involves aggregating historical tread values for multiple tires and associated parameter values defined hierarchically from a high level. By correlating current tread values with the hierarchical data, tire wear rates can be predicted for individual tires. This allows more accurate and timely tire wear prediction compared to relying solely on tire-level measurements. The hierarchical aggregation provides enough data points to make reliable wear predictions faster than waiting for multiple measurements on individual tires.

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Estimating uneven tire wear using machine learning to provide earlier detection of uneven wear compared to just measuring tread depth. The method involves obtaining an image of the tire tread, inputting it to a trained machine learning model, and deriving an output representing the estimated uneven wear. The model learns from labeled images of tires with known uneven wear levels.

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Method for predicting the wear of tires on real vehicles using both real vehicle data and indoor tire testing data. The method involves receiving tire, vehicle, wear condition, and initial mileage/wear information. It analyzes this data using a random forest machine learning algorithm to predict the final tire wear and life based on the initial values. This allows faster wear prediction compared to actual vehicle testing. The random forest algorithm learns wear trends from the indoor and real data, then applies that knowledge to predict final wear based on initial values.

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A tire wear estimator for vehicles that accurately predicts tire wear over time by aligning the conditions that affect tire wear. The estimator acquires dynamic load radius information, load information, tire pressure, and travel distance. It estimates tire wear per distance using the dynamic load radius, aligning the load and pressure conditions to suppress wear estimation variability. This improves wear prediction accuracy compared to just using current load and pressure.

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System and method for forecasting optimal tire replacement based on predicted wear states. The system uses sensors on the tire to measure footprint length and pressure, along with vehicle parameters. An AI wear state predictor estimates tire wear. A forecasting model predicts future wear states and generates an optimal replacement date when wear exceeds a threshold. This allows proactive tire replacement scheduling before minimum wear is reached.

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Estimating tire tread wear in real time using vehicle sensors and dynamics data without needing dedicated tire wear sensors. The method involves indirectly estimating tire wear based on vehicle speed, yaw rate, steering angle, wheel speed, tire pressure, and temperature. The indirect estimates are fused with occasional direct tire wear measurements to refine the wear calculation. This provides a continuous tire wear monitoring system that leverages existing vehicle sensors instead of adding dedicated tire wear sensors.

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Estimating tire wear more accurately by using vehicle data and passenger position to improve the wear calculation model. The system acquires data on cargo weight and passenger position in the vehicle. It calculates the weight distribution of the load in each section separated by stops based on the passenger positions. This weight distribution is then fed into the tire wear calculation model to estimate tire wear more accurately compared to just using vehicle weight and speed data.

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Tire wear monitoring with the capability to estimate tread wear and predict Remaining Tread Material (RTM) of tires of motor vehicles (e.g., vehicles fitted with internal combustion engines, hybrid vehicles, electric vehicles, etc.). The monitoring includes determining a calibrated Tread Wear Model (TWM) based on the measured tread-wear-related and first frictional-energy-related quantities and performing a remaining tread material prediction comprising predicting remaining tread material of the given tire of the motor vehicle by computing a remaining tread depth based on the computed tread wear value and an initial tread depth.

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Estimating tire wear using vehicle data like distance and acceleration without physical sensors. The method involves calculating a severity score based on acceleration squared, then inputting distance and severity to a wear model to estimate tire wear. This allows estimating wear without sensors by leveraging vehicle data. The severity score improves accuracy by capturing harsh acceleration events that contribute to wear.

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Method to quickly evaluate tire wear performance by weighting wear energy based on driving condition frequency instead of calculating wear for all conditions. The method involves running a vehicle under multiple conditions like free rolling, braking, driving, turning, measuring tire wear energy for each condition, and then weighting the condition-specific wear energies by their frequency of occurrence to get the overall tire wear. This reduces evaluation time compared to calculating wear for all conditions.

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Assessing tire condition for a vehicle to determine if it's suitable for a planned route. The method involves calculating predicted tire wear based on the route and vehicle data, then comparing it to the current tire condition to evaluate risk. It factors in variables like road type, steering, uphill driving, tire pressure, temperature, and load. This helps determine if a worn tire can safely complete the route versus replacing it.

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Estimating tire wear more accurately by incorporating tire temperature and vehicle acceleration into the wear calculation model. The system acquires tire temperature, vehicle mileage, and acceleration. It calculates tire wear severity based on temperature and acceleration. Then, using the severity and mileage, it estimates tire wear using a learned wear calculation model. This improves wear estimation compared to just using mileage.

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A system for estimating tire wear using vehicle data and a learned model. The system acquires vehicle information like tire temperature, altitude, and mileage. It calculates tire wear severity based on temperature and altitude. Then it estimates tire wear using a learned model with the mileage and severity as inputs. This improves tire wear estimation accuracy compared to just using mileage.

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