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Tire states and capacity monitoring is critical for vehicle and wheel stabilization controls in automated driving and active safety systems. Tire capacity, which represents the performance margin of tire forces from its limits, determines the operational range for vehicle control systems and their actuation through steering or torques at each tire to maintain stability while performing trajectory following. This paper presents a generic tire capacity identification framework that can handle different normal loads, road surface friction, and combined-slip driving scenarios, which are challenging for stabilization and tracking control programs in automated driving systems. A novel measuring method for generating force-training data is designed by combining the indoor tire test procedure and tread rubber friction test rig, in order to obtain adequate and high-quality benchmark datasets. The results from large data sets from road experimenting and indoor tire test facilities, including pure- and combined-slip conditions, confirm effectiveness of the developed learning-based tire capacity estimation which utilizes notions from the model description with bounded uncertainty. More importantly, the proposed method can provide reliable tire properties ranging from the linear to the sliding regions. Further validation is performed on a real test car with on-board sensory measurements, and the results confirm accuracy of the proposed method for various free rolling and hard launch/brake scenarios. [ABSTRACT FROM AUTHOR]

Understanding the dynamic behavior of tires and their interactions with roads plays an important role in designing integrated vehicle control strategies. Accordingly, having access to reliable information about tire–road interactions through tire-embedded sensors is desirable for developing enhanced vehicle control systems. Thus, the main objectives of this research are: 1) to analyze data from an experimental accelerometer-based intelligent tire acquired over a wide range of maneuvers, with different vertical loads, velocities, and high slip angles and 2) to develop a lateral force predictor based on a machine learning tool, more specifically, the Gaussian process regression (GPR) technique. It is determined that the proposed intelligent tire system can provide reliable information about the tire–road interactions even in the case of high slip angles. In addition, lateral force models based on GPR can predict forces very well, outperforming other machine learning models and providing levels of uncertainty that can be useful for designing vehicle control strategies. [ABSTRACT FROM AUTHOR]

The concept of intelligent tires has drawn the attention of researchers in the areas of autonomous driving, advanced vehicle control, and artificial intelligence. The focus of this paper is on intelligent tires and the application of machine learning techniques to tire force estimation. We present an intelligent tire system with a tri-axial acceleration sensor, which is installed onto the inner liner of the tire. Neural Network techniques are used for real-time processing of the sensor data. The accelerometer is capable of measuring the acceleration in x,y, and z directions. When the accelerometer enters the tire contact patch, it starts generating signals until it fully leaves it. Simultaneously, by using MTS Flat-Trac test platform, tire actual forces are measured. Signals generated by the accelerometer and MTS Flat-Trac testing system are used for training three different machine learning techniques with the purpose of online prediction of tire forces. It is shown that the developed intelligent tire in conjunction with machine learning is effective in accurate prediction of tire forces under different driving conditions. The results presented in this work will open a new avenue of research in the area of intelligent tires, vehicle systems, and tire force estimation. [ABSTRACT FROM AUTHOR]

Tire slip angle is a vital parameter in tire/vehicle dynamics and control. This paper proposes an accurate estimation method by the fusion of intelligent tire technology and machine-learning techniques. The intelligent tire is equipped by MEMS accelerometers attached to its inner liner. First, we describe the intelligent tire system along with the implemented testing apparatus. Second, experimental results under different loading and velocity conditions are provided. Then, we show the procedure of data processing, which will be used for training five different machine learning techniques to estimate tire slip angles. The results show that the machine learning techniques, especially in frequency domain, can accurately estimate tire slip angles up to 10 degrees. More importantly, with the accurate tire slip angle estimation, all other states and parameters can be easily and precisely obtained, which is significant to vehicle advanced control, and thus this study has a high potential to obviously improve the vehicle safety especially in extreme maneuvers. [ABSTRACT FROM AUTHOR]

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning (ML) algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the reference value of slip ratio can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, ML algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which creates new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation. • An intelligent tire system is presented to develop a novel slip ratio estimation method. • Important features are identified by analyzing the correlation between the accelerations and slip ratio. • Different machine learning algorithms are trained to build the slip ratio estimation models. • The proposed method can accurately estimate the slip ratio range up to ± 30% under different working conditions. [ABSTRACT FROM AUTHOR]