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.

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.

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.