Tread Wear Prediction for Tire Development

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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Optimizing vehicle motion control to improve energy efficiency by considering tire parameters. The method involves estimating tire parameters like rolling resistance, wear rate, etc based on input data. A tire model is configured using these parameters to relate tire behavior to vehicle motion. This allows estimating effects like rolling resistance for different control strategies. The vehicle is then moved using the tire model to select options with lower rolling resistance for better efficiency.

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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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