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In this paper, an intelligent tire system is designed to estimate tire force by detecting the tire carcass deformation. The intelligent tire system includes a set of marker points on the inner liner of the tire to locate the position of tire carcass and a camera mounted on the rim to capture the position of these points under different driving conditions. An image recognition program is used to identify the coordinates of the marker points in order to determine the deformation of the tire carcass. According to the tire carcass stiffness test and the general tire carcass deformation theory, an approximate linear relationship between tire force and carcass deformation in all directions was obtained. The vertical force of the tire is determined by the distance between adjacent marker points. The longitudinal force and lateral force of the tire are estimated by measuring the longitudinal and lateral displacements of the marker points.

This paper proposes a dynamic obstacle avoidance system to help autonomous vehicles drive on high-speed structured roads. The system is mainly composed of trajectory planning and tracking controllers. The potential field (PF) model is introduced to establish a three-dimensional potential field for structured roads and obstacle vehicles. The trajectory planning problem that considers the vehicle’s and tires’ dynamics constraints is transformed into an optimization problem with muti-constraints by combining the model predictive control (MPC) algorithms. The trajectory tracking controller used in this paper is based on the 7 degrees of freedom (DOF) vehicle model and the UniTire tire model, which was discussed in detail in previous work [25, 26]. The controller maintains good trajectory tracking performance even under extreme driving conditions, such as roads with poor adhesion conditions, where the car’s tires enter the nonlinear region easily.

The vehicle pull problem is very important to driving safety. Major factors that may cause the pull problem related to tire include variations of geometric dimension (e.g. RPK) and stiffness (e.g. cornering stiffness, aligning stiffness), plysteer and conicity. In previous research, the influencing mechanism of these factors was well studied. But in fact, vehicle pull problem caused by tire is probabilistic. When we assemble four tires onto the car, there could be 384 different assembly arrangements. If there are significant differences among these four tires, there will also be significant differences in the influence of different tire assembly schemes on vehicle pull, which has not been systematically discussed in previous studies. If we want to evaluate the pull performance of all these arrangements by vehicle test, it will be a time consuming process which will take almost 24 working days, along with a high test expense.

A high-precision steady state tire model is critical in the tire and vehicle matching research. For the moment, the popular Magic Formula model is an empirical model, which requires the pure and combined test data to identify the model parameters. Although MTS Flat-trac is an efficient tire test rig, the long test period and high test cost of a complete tire model tests for handling are yet to be solved. Therefore, it is necessary to explore a high accuracy method for predicting tire complex mechanical properties with as few test data as possible. In this study, a method for predicting tire combined slip characteristics from pure cornering and pure longitudinal test data has been investigated, and verified by comparing with the test data. Firstly, the prediction theory of UniTire model is introduced, and the formula for predicting combined slip characteristics based on constant friction coefficient is derived.

In this paper a tire model for describing tire turn slip properties is derived. The tread of the contact patch is divided into many massless elastic elements in both the length and width direction. Carcass deformation is expressed by the translation, bending and twisting function. A turn slip tire model is derived by analyzing the geometric relationships among the deformation of contact patch, tread and carcass. The model is validated by experimental results of parking maneuver. The model seems capable of generating transient and steady state forces and moments for turn slip, and showing varied trend of tire force according to different turn slip velocity. It could not only describe the tread deformation, but also analyze how the tread deformation affects the tire force and moment properties.

The properties of contact patch are key factors for tire modeling. Researchers have paid more attention to the contact patch shape and vertical pressure distribution. Some innovative concepts, such as Local Carcass Camber, have been presented to explain special tire modeling phenomena. For a pragmatic tire model, a concise model structure and fewer parameters are considered as the primary tasks for the modeling. Many empirical tire models, such as the well-known Magic Formula model, would become more complex to achieve satisfactory modeling accuracy, due to increasing number of input variables, so the semi-empirical or semi-physical modeling method becomes more attractive. In this paper, the concept of Tire Carcass Camber is introduced first. Different from Local Carcass Camber, Tire Carcass Camber is an imaginary camber angle caused only by lateral force on the unloaded tire.

The objective of this paper is to enhance the accuracy of tire model combined tire cornering and braking forces with anisotropic tread and carcass stiffness. The difference of tire longitudinal slip stiffness and cornering stiffness will arouse that the direction of tire resultant shear stress in adhesion region is not the same as that in sliding region. Then the direction of total friction force in the whole tire-road contact patch will change under different combined cornering/braking situations. Generally speaking, there is a basic premise: “the direction of resultant shear stress in sliding region will be the same as that in adhesion region” in the existing tire models, in which the anisotropy of tread and carcass stiffness is neglected. Therefore, these models don't work well when the tire tread and carcass stiffness has a strong anisotropy.