Toward Human-Centric Autonomous Vehicle Control: A Systematic Model-Based Approach to Motion Sickness Mitigation and Path Tracking

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.