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.
