ML-Driven Predictive Modeling for Tire Performance

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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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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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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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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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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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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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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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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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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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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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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