This paper presents an in-depth analysis of the application of different techniques for vehicle state and tyre force estimation using the same experimental data and vehicle models, except for the tyre models. Four schemes are demonstrated: (i) an Extended Kalman Filter (EKF) scheme using a linear tyre model with stochastically adapted cornering stiffness, (ii) an EKF scheme using a Neural Network (NN) data-driven linear tyre model, (iii) a tyre model-less Suboptimal-Second Order Sliding Mode (S-SOSM) scheme, and (iv) a Kinematic Model (KM) scheme integrated in an EKF. The estimation accuracy of each method is discussed. Moreover, guidelines for each method provide potential users with valuable insight into key properties and points of attention.

In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

This paper presents a comprehensive literature review on tyre force estimation and road grip recognition approaches. With the development of modern automotive control systems, a precise estimation of a large number of vehicle states is necessary to guarantee a robust actuation of the controller. Moreover, the estimation of these states must be carried out in a cost-effective and reliable way. The aim of this work is to provide a solid base for the development of automotive virtual sensors, and in particular, virtual tyre force sensors. An initial overview of the tyre force estimation problem is provided in the first section. Tyre and vehicle modelling, as well as observers for vehicle state estimation, are covered in detail in the second section. The third section introduces a brief discussion regarding the main limitations of direct tyre force measurement approaches. In the following sections, relevant works regarding three-axis tyre force estimation and road grip recognition are discussed. The review is structured around longitudinal force estimation, lateral force estimation, combined tyre force estimation, tyre self-alignment torque estimation and vertical tyre force estimation. Within each section, the most significant road grip identification approaches are introduced. An additional section, Road slope and bank angle compensation, describes relevant work on estimation methods for global chassis orientation. A brief summary of the presented approaches is provided in the section Summary of presented approaches. Finally, relevant conclusions and further research steps are given in the last section.

This paper presents a robust virtual sensor to estimate the chassis planar motion states and the tyre forces during agile manoeuvres using a tyre-model-less approach. Specifically, virtual sensing is achieved from standard sensor signals available on the CAN bus of modern vehicles using a modular filter architecture composed of Stochastic Kalman Filters. A high-fidelity virtual testing environment is constructed in IPG CarMaker® using a driver-in-the-loop setup to verify the virtual sensor without compromising driver's safety. Moreover, road random profiles are incorporated into the virtual road to assess the state estimator robustness to high vertical excitation levels. The virtual sensor is simulated under drifting manoeuvres performed by an experienced test driver and tested experimentally under Fishhook and Slalom manoeuvres. Finally, the state estimator is integrated into a drift controller and autonomous drift control using exclusively readily available measurements is verified for the first time. As the drift equilibrium depends strongly on the tyre-road friction, an Adaptive Neuro-Fuzzy Inference System has been integrated into the virtual sensor structure to provide a continuous approximation of the road friction characteristics (axle lateral force versus slip curve) in rigid and loose surfaces. The findings suggest that it may be possible to develop advanced vehicle controllers without using a tyre model. This can lead to a substantial acceleration of development time, particularly in off-road applications, and remove the need for online estimation of tyre properties due to pressure, wear and age.