Friction between rubber and rough surfaces is a critical topic for vehicle safety, yet it remains unclear which roughness parameters govern it. While the power spectral density (PSD) describes the wavelength content of surface asperities, it is not known whether PSD alone predicts friction or if the height distribution (PDF) also plays a significant role. To address this, we performed dry-contact and boundary-lubricated friction tests on 3D-printed surfaces designed to share identical PSDs but different PDFs. This approach allowed us to isolate the effect of height distribution while keeping the spectral content constant. The tests revealed substantial differences in total friction despite identical PSDs, demonstrating that the PDF and related roughness parameters significantly influence rubber–road interaction. To support the interpretation of the experimental findings, the results were further compared with spectrum-based friction theories and qualitative Boundary Element Method (BEM) contact simulations. While PSD-based approaches correctly reproduced the hysteretic trends, the BEM analysis revealed systematic differences in contact-area evolution among surfaces sharing identical PSDs but different PDFs, showing qualitative consistency with the experimentally observed friction ranking. Direct measurements of the contact area using pressure-sensitive films further confirmed these differences, suggesting that contact-area evolution is the main mechanism underlying the observed dry-friction variability. These findings provide new experimental evidence beyond the PSD and have implications for tyre design and predictive friction models.

Accurate vehicle state estimation is essential for advanced driver-assistance systems (ADAS) and autonomous driving applications, enabling robust control performance under diverse operating conditions. In this context, it becomes even more critical to design generalizable tools capable of adapting to any vehicle, operating condition, or environment: for this reason, model-based approaches become a true necessity, especially when relying on the outputs of such algorithms in closed-loop control and safety applications, making these methodologies more suitable than purely data-driven approaches. However, in current literature, there exists a variety of model-based estimation strategies with different levels of complexity, accuracy, and computational requirements. This paper reviews approximately 200 studies and presents a comprehensive review of model-based vehicle state estimation techniques, with a focus on their formulation, underlying physical assumptions, and associated trade-offs. Estimation strategies are categorized into pure kinematic, vehicle- and tire-model-based, hybrid, and more complex multibody-model-based approaches, thereby facilitating informed selection of the most suitable method to support design choices based on sensor availability and application requirements. Within each category, a more detailed analysis examines estimation strategies with variable modeling assumptions, observer architectures, and sensor configurations, emphasizing how design choices affect accuracy, robustness to parameter variations, computational cost, and sensitivity to measurement inaccuracies. In contrast to previous surveys, this work provides a structured comparative assessment of all methodologies using specifically defined evaluation criteria, enabling a direct comparison across different estimation approaches and highlighting their respective advantages and limitations. The review concludes by outlining current limitations in the state of the art and identifying promising research directions, including the integration of currently unmodeled physical effects into estimation algorithms, the necessary steps toward simplified calibration and self-calibration procedures even in detailed modeling approaches, and the exploitation of emerging sensor technologies.

Autonomous Vehicles (AVs) offer unprecedented opportunities to design control strategies that could be able to simultaneously enhance safety, performance, user experience, time efficiency, and the environmental impact of mobility. However, as automation levels increase, a paradigm shift becomes not only necessary but imperative: the integration of human needs into mobility objectives. This includes not only traditional comfort considerations but also minimizing Motion Sickness (MS), a largely under-explored challenge in control strategy design. In recent literature, several methodologies for modeling and mitigating MS have been proposed, yet their integration into vehicle control logics remains limited, often restricted to isolated and specific case studies, with the research area largely unexplored, particularly with respect to the generalization of the proposed methods. This work introduces a theoretically grounded multi-objective Nonlinear Model Predictive Control (NMPC) framework for coupled vehicle–passenger systems, featuring a novel prediction horizon optimization methodology and adaptive conflict resolution strategies for heterogeneous performance metrics to mitigate motion-induced discomfort while ensuring accurate path tracking. Human-centric control design is pursued by embedding increasingly complex vehicle models and MS metrics, further addressing the trade-off between model fidelity and computational feasibility, and introducing a methodological standpoint for selecting the optimal prediction horizon in the presence of heterogeneous and conflicting control objectives, an aspect often overlooked in current literature. An experimental campaign supports model calibration and validation, while multi-scenario simulations demonstrate the framework’s ability to balance tracking performance, computational efficiency, and passenger comfort.

Accurate knowledge of surface roughness is essential for understanding key phenomena such as tire-road friction and wear rate. However, high-resolution surface characterization typically requires scanning devices that are time-consuming to use and expensive. In time-critical contexts like motorsport, where setup windows are narrow, having a faster and more accessible alternative could provide a significant advantage. This paper presents a fast, cost-effective and physically interpretable methodology to reconstruct the three-dimensional roughness of road surfaces directly from grayscale images. The results demonstrate a strong correlation between 2D photometric information and 3D surface features, preserving both the height distribution and spectral content. The proposed framework combines light-intensity features extracted from the image with a Gaussian Process Regression (GPR) model, offering high physical controllability and reduced data requirements compared to deep learning approaches such as convolutional neural networks. The model was validated under various conditions and proved effective even on unconventional textures, including sandpaper and marble samples. It also showed robustness to different image formats. Overall, the method provides a practical and innovative alternative for surface roughness assessment, with strong potential for applications in vehicle dynamics, tire modeling and tribology.

Tire wear arises from coupled viscoelastic deformation, interfacial friction, temperature, and material fatigue, with growing relevance for performance, durability, and environmental impact. This review synthesizes the main families of tire-wear models, including empirical, analytical, numerical, and data-driven approaches, and examines their physical assumptions, predictive range, computational cost, and applicability to realistic tire road conditions. Emphasis is placed on friction and wear interactions, rubber specific complexities, and the absence of a common tribological framework. This work offers three key contributions: a systematic classification of modeling approaches and their assumptions; integration of environmental and regulatory drivers relevant to tire wear assessment; and an evaluation of model applicability across validation contexts, from laboratory tests to indoor rigs and real world conditions. Remaining challenges include transient dynamics, thermo-mechanical coupling, and model generalization. Future directions highlight the need for physically grounded and efficient models able to bridge scales, materials, and applications.

Current Autonomous Vehicles (AVs) guidelines require increasingly stricter safety standards and aim towards developing sophisticated control logics to achieve multiple objectives in various scenarios. Indeed, major attention has been oriented towards meeting safety standards in high-velocity profile contexts where the reduction of accidents is closely related to the design of more accurate vehicle control logics. However, as the vehicle represents a intrinsically dynamic mechanical system, its operating (mass, tyre thermal and wear state) and external (weather) conditions continuously change and the performance of the controller can rapidly degrade, leading to increased safety-related risks and decreased comfort perception. This is mainly due to standard controllers not sharing their parameters with the vehicle since their calibration is usually performed in pre-defined conditions. To assess the impact of varying tyre and vehicle-related conditions, the path tracking scenario has been considered as it currently represents one of the most representative objectives when designing AVs control logics. For that purpose, a standard five-states lateral bicycle vehicle model with a Magic Formula (MF) model for tyre forces evaluation, has been employed in a Model Predictive Controller (MPC) framework with front steering as control signal. To the purpose of describing tyre wear state, a dedicated model considering the necessary working and boundary conditions, developed by the research group, has been considered. Various highway trajectories, at different speeds and road curvatures, have been simulated on a high-fidelity reference model of the real vehicle, employed as plant model. Simulations have been carried out by means of primary and secondary metrics: the former with the aim of evaluating path tracking performance through vehicle dynamics safety- and comfort-related indexes; while the latter employing tyre wear model to assess the degradation and overall environmental impact of the designed control logic.

Maximising tyre performance requires balancing conflicting targets, grip, wear resistance, and rolling efficiency, while accelerating development. In this context, tribological characterisation at compound level supports faster prototyping and reduces reliance on full-scale testing. Although standards for rubber friction testing exist, they are rarely followed in literature, and procedures are often underreported. This work addresses that gap by presenting the complete development of an experimental framework for rubber friction and wear testing, with particular focus on tyre tread compound, from the definition of functional requirements to the design of a novel linear friction tester and the implementation of a robust testing methodology. The Ground Rubber Interface Performance (GRIP) tester was designed for high versatility and cost-effectiveness. A key feature is the open-access architecture, which allows practical surface management and rapid retooling. A custom back-heating system ensures uniform specimen temperature even under varying test conditions. The methodology focuses on critical but overlooked aspects: specimen conditioning, surface rubberisation, and temperature control. Case studies demonstrate the repeatability of results and the system’s sensitivity to key input parameters. Additional tests confirm the platform’s adaptability to non-tyre tribological applications.

Direct topographic profiling of real pavement surfaces is often challenging, especially on high-traffic highways racetracks where access time is limited. In such cases, silicone replicas offer a practical alternative for acquiring detailed surface information related to frictional behavior, wear mechanisms, and the mechanical response materials in contact. In this work, a quantitative comparison is presented between topographic measurements carried out on two road specimens and the corresponding silicone replicas produced via dedicated molding, alongside methodological guidelines for the correct use of silicone replicas and the processing of the acquired topographic data. 3D areal parameters according to ISO 25178-2, such as the power spectral density (PSD), probability density function (PDF), and the Abbott-Firestone curve, are compared between the silicone replica and the drill road surface to validate whether the replica can reliably reproduce the surface information. comparison between silicone and the road specimens underlines that the technique must be employed with care, since differences attributable to "mushroom-shaped" cavities can lead to considerable errors in the roughness parameters.

The anticipated rise in the adoption of autonomous vehicles (AVs) has highlighted the need to redesign vehicle control architectures. This involves compromises guaranteeing performance and safety while addressing passenger comfort, which becomes increasingly critical as passengers shift from active drivers to passive occupants, making them vulnerable to motion sickness (MS). Despite the significant number of research studies examining MS mechanisms, quantification methods, and mitigation strategies, current control approaches seldom incorporate passenger states into the vehicle control loop. Nevertheless, a review of the literature reveals that existing definitions of a “holistic controller” are either vague or fragmented. Acknowledging the necessity to harmonize diverse methodologies and delineate a consolidated definition of a holistic framework, this review initiates with a thorough exposition of vehicle-related state estimation and control methodologies, accentuating the proposed literature solutions on holistic approaches. A critical distinction emerges between traditional integrated control, relying on separate, loosely-coupled modules with limited inter-module communication and vehicle-centric optimization, and the proposed holistic control featuring a multi-level architecture with bidirectional information flow, adaptive parameter weighting, and simultaneous consideration of vehicle and passenger subsystems to achieve multi-objective optimization encompassing safety, comfort, and overall efficiency. Additionally, a comprehensive discourse on the necessary additional module regarding the passenger state estimation techniques is presented, with a particular emphasis on those targeting head motion, which is closely associated with the onset of MS, with the following discussion focused on the sensing strategies employed in relation to the underlying vehicle estimation frameworks. In light of the aforementioned insights, the paper proposes the concept of a holistic controller, defined as a multi-level structure that collectively considers heterogeneous subsystems to achieve multi-objective optimization by managing trade-offs between competing objectives. Finally, the requirements and feasibility of such a framework in real-world applications are discussed, outlining how current research is evolving, defining the incoming demand for modular frameworks, high-performance computing, and shared solutions.

Automotive signal processing is dealt with in several contributions that propose various techniques to make the most out of the available data, typically for enhancing safety, comfort, or performance. Specifically, the accurate estimation of tire–road interaction forces is of high interest in the automotive world. A few years ago the T.R.I.C.K. tool was developed, featuring a vehicle model processing experimental data, collected through various vehicle sensors, to compute several relevant virtual telemetry channels, including interaction forces and slip indices. Following years of further development in collaboration with motorsport companies, this article presents T.R.I.C.K. 2.0, a thoroughly renewed version of the tool. Besides a number of important improvements of the original tool, including, e.g., the effect of the limited slip differential, T.R.I.C.K. 2.0 features the ability to exploit advanced sensors typically used in motorsport, including laser sensors, potentiometers, and load cells installed on shock absorbers, anti-roll bars, and brake pressure sensors. Such information is harnessed in purposely-devised novel methodologies for estimating key quantities including roll angle, aerodynamic forces, and camber angle, all affecting tire–road interaction forces and friction ellipses. This is made possible by a completely modular structure of the tool able to employ the most accurate formulation depending on the sensors actually available.

Recent advancements in control technologies, sensor availability and intelligent actuators have driven the development of Integrated Vehicle Dynamics Control (IVDC). This approach offers a unified framework for managing complex interactions between subsystems, enabling enhanced safety, performance and adaptability features. However, practical implementation and widespread diffusion of this technology remain a significant challenge due to the lack of standardized strategies and heterogeneity of components, with a significant gap between academic descriptions and automotive industry practices became evident through analysis of over 300 sources from scientific literature, industrial patents, and OEM documentation. This work aims to investigate the applicability of IVDC within existing vehicle architectures and address the need for a real-time adaptive control solution. A systematic examination of subsystem coupling mechanisms, industrial applications and the influence of different vehicle morphologies on the choice of control architecture are discussed. Moreover, alternative approaches, such as decoupled control and multi-agent systems, are introduced as potential solutions to overcome limitations of conventional coordination schemes. Finally, emerging perspectives on system adaptability, with particular emphasis on stability-oriented design and the enabling role of Vehicle-to-everything (V2X) communication, are discussed. Even considering a limited availability of comparable quantitative metrics from OEMs due to proprietary considerations, the overall intention is to provide a comprehensive and pragmatic outlook on the evolving boundaries of IVDC, based on rate of usage metric as a function of actuator type and availability, considering its alignment with current and future market demands.

This review surveys theoretical frameworks developed to describe rubber contact and friction on rough surfaces, with a particular focus on tire–road interaction. It begins with classical continuum approaches, which provide valuable foundations but show limitations when applied to viscoelastic materials and multiscale roughness. More recent formulations are then examined, including the Klüppel–Heinrich model, which couples fractal surface descriptions with viscoelastic dissipation, and Persson’s theory, which applies a statistical mechanics perspective and later integrates flash temperature effects. Grosch’s pioneering experimental work is also revisited as a key empirical reference linking friction, velocity, and temperature. A comparative discussion highlights the ability of these models to capture scale-dependent contact and energy dissipation while also noting practical challenges such as calibration requirements, parameter sensitivity, and computational costs. Persistent issues include the definition of cutoff criteria for roughness spectra, the treatment of adhesion under realistic operating conditions, and the translation of detailed power spectral density (PSD) data into usable inputs for predictive models. The review emphasizes progress in connecting material rheology, surface characterization, and operating conditions but also underscores the gap between theoretical predictions and real tire–road performance. Bridging this gap will require hybrid approaches that combine physics-based and data-driven methods, supported by advances in surface metrology, in situ friction measurements, and machine learning. Overall, the paper provides a critical synthesis of current models and outlines future directions toward more predictive and application-oriented tire–road friction modeling.

Tire-road interaction involves complex phenomena related to contact modeling and the evaluation of the friction coefficient in different conditions. Over the years, many approaches have been followed to develop physical models capable of considering all the relevant parameters, such as viscoelastic properties, road roughness and tire conditions. In this scenario, contact modeling is a fundamental topic as the effective contact area, when the tire is in contact with the road, is smaller than the nominal area due to the indentation of the rubber on road asperities. This paper presents an extended version of the Greenwood-Williamson contact model able to evaluate the ratio between the real contact area and the nominal one (Ac/A0), exploiting a complete non-destructive characterization procedure for the evaluation of the tire tread viscoelastic properties and an innovative method to estimate the road roughness descriptors.

In the automotive industry, tyre friction plays a critical role in maximizing safety and performance, while reducing total vehicle energy consumption. Rubber friction is a phenomenon still not totally understood, since it is influenced by a vast array of linked factors, involving polymer viscoelastic characteristics, surface roughness, and the conditions under which the contact occurs. In this context, the most reliable way to quantify the friction arising at the tread-road contact is empirical analysis via specific test benches called friction testers. This paper presents a new linear friction tester (LFT) for friction and wear tests developed by the Applied Mechanics Research Group of the Department of Industrial Engineering of the University of Naples "Federico II". The rig was designed to strike a balance between production costs and versatility of use, aiming to accurately reproduce rubber-substrate interactions over a wide range of working conditions. Along with the machine, a testing methodology has been developed and applied to a first case study, in order to demonstrate the degree of repeatability that can be achieved. A tyre tread compound has been tested under various sliding velocities, temperatures, and contact pressures, paying particular attention to any deviation between the runs. The preliminary results indicate the high promise of the device in providing crucial data for validating analytical friction models presented in scientific literature.

The optimization of vehicle handling is a multifaceted process that extends beyond the vehicle’s design and engineering. This work focuses on the fundamental role that drivers play in shaping the vehicle’s overall behavior. While technological advancements have significantly impacted the automotive industry, defining new methodologies and approaches for vehicle controls, there is not yet a uniquely recognized procedure to objectively define the skills and weaknesses of pilots. This paper aims to present the preliminary results of an innovative study, based on an outdoor test campaign with a fully instrumented vehicle, driven on track by several drivers with different levels of experience. Starting from the collected data, a series of objective and generalized metrics have been defined in order to quantify different aspects related to the direct driver interaction with the car and to the trajectory repeatability. By analyzing the results obtained from these metrics, it has been possible to highlight the differences among the participants in the experimental campaign. In order to create a practical visualization of the goodness of the approach, a driver ranking has been defined and it is coherent with both the best lap times obtained by the drivers and their actual experience.

Contact modeling plays a crucial role in tire road interaction, impacting several fields, including vehicle dynamics, road safety, and transportation efficiency. As the tire is in contact with the road, the real contact area is smaller than the nominal area due to the indentation of the rubber over the road profile, influenced by the distribution of road texture. This paper introduces a novel approach to contact modeling, focusing on the evaluation of the ratio between the real contact area and the nominal one (A(c)/A(0)), considering the Greenwood-Williamson formulation. This ratio is fundamental for characterizing the tire-road contact behavior, as it depends on tire viscoelastic properties, road roughness characterization and tire operating conditions. The paper presents some simulations conducted in MATLAB to assess the A(c)/A(0) ratio for various road specimens. These simulations were conducted considering a specific compound, while varying parameters such as contact pressure and compound temperature, with a fixed sliding velocity. This research, thus, enhances the understanding of how the road texture, combined with the tire properties and operating conditions, affects tire indentation over the road profile which is strictly related to the perceived friction values.

In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls.

In vehicle dynamics, the study of the viscoelastic characterization of tires is crucial for a better understanding of the adhesion phenomenon during tire-road interaction, with the ultimate goal of enhancing vehicle performance and safety. The viscoelastic characterization of polymeric specimens traditionally involves costly and destructive practices. However, the development of the VESevo device has allowed to overcome these limitations for tire viscoelasticity characterization. This paper presents an analysis on tire adhesion by considering three different compounds tested with the VESevo device, to compare the acquired curves with and without the use of talc on the compound. Additionally, since the adhesion phenomenon is dependent on temperature, the various specimens were heated to explore the thermal effect. The paper investigates the adhesion variances among soft, medium, and hard compounds under changing temperature and compound conditions, commencing with rebound curve comparisons with and without talc.

In the development of physical tire models, the complexity of the composite structure and the multiphysical variables require strongly nonlinear mathematical formulations to guarantee a desired degree of accuracy. The aim of the current work is to extend the applicability of the multiphysical magic formula-based tire model, already developed and presented by the authors, within a wider frequency range, interposing a rigid ring body between the contact patch and the wheel hub. The contact patch, varying in terms of size, shape, and relative position, is evaluated using instantaneous cams to define the effective plane. Here the advanced slip model, taking into account thermodynamic and wear effects, is then integrated. The adopted formulations have been mathematically and physically justified. They have been analytically compared to formulations related to the rigid-ring implementation available in the literature. Specific experimental activities concerning both the tire’s vertical kinematics and dynamics have been conducted to demonstrate the model’s improved physical consistency on small wavelength unevennesses.

Vehicle state estimation plays a crucial role in the design and development of advanced systems for vehicle control and autonomous driving applications. In this context, the knowledge of vehicle side slip angle is required to optimize lateral dynamics control, improving the overall handling performance. On one hand, the direct measurement of lateral velocity can only be provided by employing sophisticated and expensive onboard sensors. On the other hand, the vehicle system and, in particular, the tires can significantly modify their behaviour through time due to temperature, pressure and wear influences, thus modifying both the vehicle handling behaviour and the parameters of the installable control logic. For this reason, the authors propose an innovative estimation method, combining the vehicle mathematical implementation with a double-track model enriched with virtual observations obtained through a kinematic observer. To validate its applicability in contexts covering a wide range of tire thermodynamic conditions, the proposed estimation approach has been integrated with a multiphysical tire formulation, proposed by the authors in the previous studies, able to take into account of temperature and pressure effect on the tire dynamic response. The results have been analysed in terms of the vehicle sideslip angle acquired in a dedicated experimental campaign comparing the accuracy of the proposed approach to the commonly adopted one, not accounting for temperature and pressure influences.