Tires, being the only component of the vehicle in contact with the road surface, are responsible for generating the forces for maintaining the vehicle pose, orientation and stability of the vehicle. Additionally, the on-board advanced chassis control systems require estimation of these tire-road interaction properties for their operation. Extraction of these properties becomes extremely important in handling limit maneuvers such as Double Lane Change (DLC) and cornering wherein the lateral force transfer is dependent upon these computations. This research focuses on the development of a high-fidelity vehicle-tire model and control algorithm framework for vehicle trajectory tracking for vehicles operating in this limit handling regime. This combined vehicle-tire model places an emphasis on the lateral dynamics of the vehicle by integrating the effects of relaxation length on the contact patch force generation. The vertical dynamics of the vehicle have also been analyzed, and a novel double damper has been mathematically modeled and experimentally validated. Different control algorithms, both classical and machine learning-based, have been developed for optimizing this vertical dynamics model. Experimental data has been collected by instrumenting a vehicle with in-tire accelerometers, IMU, GPS, and encoders for slalom and lane change maneuvers. Different state estimation techniques have been developed to predict the vehicle side slip angle, tire slip angle, and normal load to further assist the developed vehicle-tire model. To make the entire framework more robust, Machine Learning algorithms have been developed to classify between different levels of tire wear. The effect of tire tread wear on the pneumatic trail of the tire has been further evaluated, which affects the aligning moment and lateral force generation. Finally, a Model Predictive Control (MPC) framework has been developed to compare the performance between the conventional vehicle models and the developed vehicle models in tracking a reference trajectory. / Doctor of Philosophy / In our rapidly advancing world, self-driving or autonomous vehicles are no longer a vision of the future but a reality of today. As we grow more reliant on these vehicles, ensuring their safety and reliability becomes increasingly critical. Unlike traditional vehicles, self-driving cars operate without human intervention. Consequently, the onus of passenger and pedestrian safety falls squarely on the vehicle's control systems. The efficiency and effectiveness of these control systems are pivotal in preventing accidents and ensuring a smooth ride. One vital aspect of these control systems lies in understanding the tires' behavior, the only parts of the vehicle that are in contact with the road surface. A tire's interaction with the road surface significantly impacts the vehicle's handling and stability. Information such as how much of the tire is in contact with the road, the forces and moments generated at this contact point, becomes valuable for optimizing the vehicle's performance. This is particularly crucial when a vehicle is turning or cornering, where the forces developed between the tires and the road are key to maintaining control and stability. In this research, a framework has been designed to improve the vehicle performance, primarily by improving the modeling of tire lag dynamics. This refers to the delay or 'lag' between a change in tire conditions (such as pressure, wear, and temperature) and the corresponding change in tire behavior. In addition, in this research a vertical dynamics model of the vehicle has also been developed incorporated with a novel double damper suspension system. To complete the entire framework, the effect of tire wear over time and how this affects its performance and safety characteristics has also been examined. By estimating and understanding this wear, we can predict how it will affect the dynamic properties of the tire, thus improving the reliability and efficiency of our autonomous vehicles. The last piece of this framework comprises the development of an MPC controller to track a reference trajectory and evaluate the performance of the developed model.

Driving with tyres not appropriate for the actual conditions can not only lead to accidents related to the tyres, but also cause detrimental effects on the environment via emission of rubber particles if the driving conditions are causing an unexpectedly high amount of tread wear. Estimating tyre performance in an online setting is therefore of interest, and the feasibility to estimate friction performance, velocity performance, and tread wear utilizing available information from the automotive grade sensors is investigated in this thesis. For the friction performance, a trend analysis is performed to investigate the correlation between tyre stiffness and friction potential. Given that there is a correlation, a model is derived based on the trend having a stiffness parameter as an input in order to predict the friction performance. Tendencies for a linear trend is shown, and a linear regression model is fitted to data and is evaluated by calculating a model fit and studying the residuals. Having a model fit of $80\%$, the precision of the expected values stemming from the proposed model is concluded to be fairly low, but still enough to roughly indicate the friction performance in winter conditions. A tread wear model that can estimate the amount of abrasive wear is also derived, and the proposed model only utilizes available information from the automotive grade sensors. Due to the model having a parameter that is assumed to be highly tyre specific, only a relative wear difference can be calculated. The model is evaluated in a simulation environment by its ability to indicate if a tyre is under the influence of a higher wear caused by a higher ambient temperature. The results indicates that the model is insufficient in an online setting and cannot accurately describe the phenomena of softer tyres having a larger amount of wear caused by a high ambient temperature compared to stiffer tyres. Lastly, a double lane change test (ISO 3888-2) is conducted to determine the critical velocity for cornering manoeuvres, which defines the velocity performance. The test was executed for six different sets of tyres, two of each type (winter, all-season, and summer). The approach to estimate the velocity performance in an online setting is analogue to that of the friction performance, and a trend analysis is performed to investigate the correlation between longitudinal tyre stiffness and the critical velocity. The results are rather unexpected and shows no substantial differences in velocity performance, even though the tyre-road grip felt distinctively worse for the softer tyres according to the driver. It is concluded that the bias stemming from the professional driver's skills might have distorted the results, and that another approach might need to be considered in order to estimate this performance.