ML Models for Predicting Tire Performance

A tire pressure monitoring system (TPMS) that uses historical tire data to predict tire failures and recommend preventive maintenance. The TPMS monitors tire temperature and pressure over time. It compares tire parameters like temperature differential and pressure decay between wheels to predict blowouts and punctures. By analyzing trends across multiple events, it can determine if a tire is approaching failure. The system alerts the user through a display on their device to inspect the tires proactively.

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Using micro-electromechanical system (MEMS) sensors inside vehicle tires to monitor tire vibrations and identify road conditions, tire wear, and vehicle issues. A neural network circuit near the sensors processes the vibration data instead of transmitting it to the main ECU. The neural network generates output indicating road conditions, tire wear, etc. This reduces ECU load and saves power compared to transmitting raw sensor data. The neural network is trained using tire vibration data from cars driven on different roads.

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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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Estimating vehicle wheel traction and stability in real-time using machine learning to improve vehicle control in varying road conditions. The method involves training regression and classification models offline using input features like wheel dynamics, acceleration, velocity, etc. to predict wheel traction limit and stability status. These models are then used onboard the vehicle to calculate predicted wheel characteristics based on sensor inputs. This allows estimating wheel performance in real-time without direct measurements, aiding control systems like traction control and ABS by providing estimated limits.

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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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Modeling a tire slip rate prediction model using artificial intelligence to accurately estimate tire slip rate in real-time without relying on vehicle speed. The model is trained using tire test data to label vertical acceleration measurements with slip rate for each revolution. This labeled acceleration data is then used to train an AI model for predicting tire slip rate from vertical acceleration. The model can be used in autonomous vehicles to improve stability, braking, and acceleration performance by providing more accurate tire slip rate estimates.

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A method for evaluating tire performance that uses machine learning to predict ride comfort and handling based on measured tire properties. The method involves acquiring tire measurements, inputting them into a predictive model, and deriving an output that allows the driver to evaluate ride comfort and handling. This output corresponds to the driver's sensory evaluation of a vehicle equipped with that tire compared to a reference tire. The model is trained using measured tire properties and sensory evaluations.

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Improving tire pressure monitoring accuracy in vehicles using machine learning. Instead of installing pressure sensors in each tire, the method determines tire pressure indirectly based on wheel speed and temperature. A tire pressure prediction model trained using historical tire data is used. The model takes wheel speed and temperature as inputs and outputs predicted tire pressure. The model can be updated over time as actual tire pressure readings are received. This allows improving model accuracy based on the specific vehicle's tires.

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Modeling an end-to-end tire performance margin identification model using vehicle data to accurately estimate tire condition without direct sensor measurements. The method involves obtaining tire performance margins from vehicle data, segmenting the labeled data, and training an AI model on the segmented data to predict tire performance margins from new vehicle data.

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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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Detecting tire lateral force using deep learning to enable tire uniformity quality control without relying on expensive equipment. The method involves training a stacked denoising self-encoder model with attention using tire production process parameters. The trained model can then predict tire lateral force from the process parameters, allowing in-line tire uniformity detection using data that is already being collected during production. This provides real-time tire quality monitoring at lower cost compared to traditional equipment-based lateral force measurement.

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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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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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Tire dynamic characteristic prediction method that can reduce the number of teacher data necessary for creating a prediction model. The method includes inputting the design factors of a plurality of tires having at least one different tire design factor into a computer; a step of inputting the static characteristics into the computer respectively; a step of inputting the dynamic characteristics of the plurality of tires into the computer; and a step of creating a prediction model capable of outputting the dynamic characteristics of the tire to be predicted from the design factors and static characteristics of the tire to be predicted, using the dynamic characteristics as training data; a step of inputting the design factors and static characteristics of the tire to be predicted; and the computer inputting the design factors and static characteristics of the tire to be predicted into the prediction model, and outputting the dynamic characteristics.

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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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Estimating tire wear more accurately by extracting features from tire sensor data using machine learning models. The system involves installing a sensor on the tire to measure physical quantities related to deformation. A learning-based model extracts features from the sensor data and estimates the tire wear index. This improves wear estimation accuracy compared to directly using the raw sensor data. The extracted features are more representative of tire condition compared to raw sensor values.

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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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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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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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Method and system for designing tires that balances performance and handling. The method involves using machine learning to train a tire model based on tire parameters and performance. By determining the target tire performance requirements, the closest matching parameters are found. Then, the tire parameters are further optimized based on vehicle handling stability. This allows designing tires tailored to specific applications while maintaining good overall performance.

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A tire physical information estimation system that accurately estimates tire forces and other parameters using a deep learning model. The system has a convolutional neural network (CNN) with input layers, feature extraction layers, and output layers. The CNN takes tire motion data as input, extracts features, and outputs estimated tire forces and other physical parameters. The CNN uses convolution operations and normalization to process the tire motion data and extract relevant features. This allows accurate estimation of directional tire forces using a learning-based approach.

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A method for improving the accuracy of tire condition estimation systems by extracting changes in tire characteristics over the life of the tire. The method involves capturing tire data like pressure, temperature, and tread depth, storing it in a historical log, applying time-series decomposition to isolate underlying trends, and using a learned model to predict tire condition based on those trends. This accounts for bias and variance in tire characteristics that change over time.

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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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Predicting real vehicle attributes like load and handling stability using machine learning models trained on tire design and dynamics databases. The method involves two prediction models. A first model predicts tire external characteristics like hysteresis loss based on tire design parameters. A second model predicts vehicle attributes like stability based on the tire external characteristic predictions. This two-stage approach improves real vehicle attribute prediction accuracy compared to directly predicting vehicle attributes from tire design.

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Modeling method for identifying tire performance margins that allows accurate determination of tire limits under all conditions, including compound working scenarios. The method involves collecting tire data like angular velocity, slip angles, forces, and loads during testing. This data is used to train a machine learning model that learns to map total slip ratios and normalized forces to the tire performance margins. The trained model can then predict tire limits from real-time vehicle data, providing insight into tire behavior and stability.

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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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Artificial intelligence-based tire wear prediction method for autonomous vehicles that can predict tire wear life and optimize tire durability through active control of vehicle dynamics. The method uses machine learning to analyze tire wear factors from real-world testing and predicts tire wear life for each tread groove. This wear data is then used to optimize vehicle parameters like braking, suspension, steering, and turning to maximize tire life. The wear predictions are provided to the vehicle's electronics for active control.

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Predicting tire wear using machine learning to enable early analysis and preemptive action on tire wear issues. The method involves collecting tire wear data from sensors in vehicles, preprocessing it, and using machine learning techniques like random forests and boosted regression to predict tire wear life. The technique links to vehicle systems for real-time tire wear analysis and active control to extend tire life. It also provides tire wear data to other devices. The machine learning models are optimized by eliminating less significant factors.

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Real-time estimation of physical tire properties like tire force and friction coefficient using a neural network model. The model takes input data from sensors to estimate tire-specific metrics related to tire contact forces and friction. The neural network has an arithmetic model with an input layer, output layer, and a feature extraction layer that applies convolution operations to extract features from the input data. This allows real-time estimation of physical tire properties without relying on vehicle dynamics and accelerometers.

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An unmanned vehicle tire monitoring method using AI to predict tire conditions and prevent failures. The method involves monitoring tire temperature and pressure in real-time, and using AI models to simulate future tire conditions based on historical data. By comparing the simulated conditions to the current readings, it can detect if the tire is at risk of overheating or overpressure. This allows early warning and intervention to prevent internal cracks or bursts. The AI models are trained using historical tire data from multiple vehicles to account for variation in tire wear and loading.

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Data-driven tire modeling method for accurate and efficient tire force prediction in vehicle dynamics. The method involves capturing tire force data during dynamic driving conditions and using machine learning techniques to build a tire model that can predict tire forces based on vehicle and road inputs. This allows more accurate and dynamic tire force prediction compared to traditional tire models built using static testing and mechanical theory. The method involves collecting tire force data during dynamic driving conditions, feature engineering to extract relevant variables, and training a machine learning model using this data to predict tire forces for different vehicle and road inputs. This provides a more accurate and dynamic tire model for vehicle dynamics applications compared to traditional steady-state tire models built using static testing and mechanical theory.

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Optimizing tire contact patches using machine learning to find the best tire configuration for a vehicle based on vehicle parameters, road conditions, and driver preferences. The method involves two machine learning models. The first model takes vehicle data like velocity, tire pressure, load, road surface, and center of gravity motion to predict optimal tire contact patch dimensions. The second model takes the predicted patch dimensions, vehicle data, and driver preferences to determine the optimal tire pressure and suspension adjustment for the load. This iterative process of predicting and adjusting tire configurations using machine learning allows finding optimal tire setups for specific driving conditions and preferences.

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Using machine learning to accurately predict tire durability in various conditions beyond just laboratory tests. The method involves assigning weights to factors like temperature, road conditions, and load, scoring them based on a formula, and training a machine learning model on the scored data. This allows quantitative analysis of environmental and vehicle effects on tire durability. The model is then used to predict tire longevity based on input conditions.

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Accurately estimating tire tread wear using only commercially available vehicle sensors without the need for additional sensors. The method involves leveraging existing sensors like wheel speed sensors, steering angle sensors, and accelerometers to estimate tire tread wear. It uses machine learning algorithms to analyze the sensor data over time to learn the relationship between tire wear and sensor readings. This learned model is then used to estimate tire tread wear based on the sensor readings. This provides a cost-effective and accurate tire tread wear estimation system using only standard vehicle sensors.

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Intelligent tire wear life estimation method and device based on BP neural networks for accurately predicting tire wear life using a neural network trained on factors like tire pressure, vehicle speed, load, and modal frequencies. The method involves acquiring tire data, splitting into training, validation, and test sets, creating a BP neural network, training it on the first sets, and estimating wear life using the trained network on the test set.

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Enhancing vehicle performance by dynamically modeling tire behavior in real-time to improve traction and cornering. The method involves using vehicle data like acceleration, steering, and thrust to predict tire performance factors like cornering limits. This allows adjusting the vehicle's lateral guidance profile based on real-time tire behavior instead of fixed conservative limits. The tire performance predictions are made using a trained neural network analyzing historical vehicle data.

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Enhancing vehicle performance by modeling tire behavior in real-time to optimize cornering and traction control. A neural network analyzes vehicle dynamics, pilot inputs, and tire forces to predict tire performance factors like cornering limits. This real-time tire modeling is used to dynamically adjust the vehicle's lateral guidance profile based on tire conditions. This allows leveraging the full tire capability instead of conservatively assuming limits.

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A method to improve the accuracy of tire condition estimation systems by extracting changes in tire characteristics over the life of the tire. The method involves using historical sensor data to model the relationship between specific tire characteristics and tire conditions. By isolating and analyzing the underlying trend in the characteristic of interest, like tread depth, while accounting for external factors, it provides more accurate predictions of tire wear state and load compared to direct measurement techniques.

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Smart wheel cap for vehicles that uses onboard sensors, machine learning, and big data to predict tire wear and unevenness. The wheel cap collects tire and vehicle data like rotation, acceleration, alignment, GPS, etc. This data is sent to a server that learns tire wear patterns using machine learning. It predicts tire wear based on driving habits and conditions. The server notifies the driver of tire wear, unevenness, and replacement needs through an app on their device. The smart wheel caps and server build a big data set of tire wear patterns from vehicle data.

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Predictive maintenance for vehicle tires using artificial neural networks to analyze sensor data over time to identify issues and recommend maintenance before failures. The technique involves measuring tire parameters with sensors, feeding the time-varying data into an ANN to analyze for issues, and generating maintenance recommendations based on the ANN results. This allows proactive tire maintenance instead of relying on fixed schedules. The ANN can learn to detect tire problems and predict maintenance needs based on historical data.

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Monitoring vehicle tire pressure in real-time while driving using onboard sensors and machine learning. The method involves acquiring vehicle driving data like video, speed, and position. This data is input into a pre-trained classification model to predict the tire pressure. By periodically checking tire pressure using the model, the actual tire pressure can be monitored without physical sensors. The model is trained on sample driving data with known tire pressures.

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A system for accurately estimating tire wear using machine learning to generate a wear calculation model from real-time tire data and vehicle position. The system acquires tire temperature, pressure, and vehicle position, calculates tire wear using a wear model, and updates the model based on actual measured wear to improve accuracy over time.

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Predicting tire wear using a cloud-based system that analyzes vehicle tire wear data. The system receives tire wear histograms from vehicles and uses trained coefficients to convert them into estimates of physical tire wear. This allows predicting tire wear without transmitting large amounts of sensor data from the vehicle. The coefficients are trained based on correlations between histograms and measured tire wear. The system sends alerts when estimated wear exceeds thresholds for tire rotation or replacement.

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A method for predicting automobile tire wear failure using large data from connected cars to provide early warning of tire wear. The method involves analyzing tire characteristics, driving behavior, and environmental factors from connected cars to accurately predict tire wear life. The data is collected from connected cars and analyzed to identify correlations between factors like tire brand, speed, road conditions, temperature, and braking. This data is used to develop a tire wear prediction model that can accurately estimate remaining tire life. The prediction is communicated to the vehicle owner via text or voice message so they can proactively replace tires before failure.

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Using artificial neural networks (ANNs) to predict tire performance metrics like rolling resistance, dynamic turning radius, and weight without physically manufacturing the tire. The method involves inputting various tire design, material, size, and environmental data into ANNs for modeling and prediction. This provides an alternative to traditional tire analysis techniques that require time-consuming, performance-specific models for each metric. The ANN approach aims to overcome the limitations of conventional tire analysis by providing a generalized, comprehensive prediction method.

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A method for reliably evaluating tire performance using a combination of case-based reasoning and artificial neural networks to provide more accurate tire performance predictions. The method involves extracting similar tire data using case-based reasoning, calculating differences between the extracted tires and the target tire, training an artificial neural network with those differences, using the neural network to predict tire performance, and comparing the predicted performance to the results of a simulation to determine if it falls within a confidence interval. This allows checking if the simulation results are within an expected range based on similar tires.

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