Reducing Discomfort in Driving Simulators: Motion Cueing for Motion Sickness Mitigation

2025-10-02

AI Summary

Motion scaling in driving simulators often mismatches visual stimuli, inducing motion sickness and limiting practical applications. To address this, we propose a novel motion cueing algorithm based on Model Predictive Control (MPC), the first to integrate a physiological Subjective Vertical Conflict (SVC) model within an MPC framework. The algorithm jointly optimizes suppression of perceptual conflict and minimization of specific force tracking error, enabling dynamic trade-offs between motion sickness mitigation and motion fidelity. Human-subject experiments conducted on a six-degree-of-freedom motion platform demonstrate that, compared to adaptive washout filtering, the proposed method reduces mean MISC scores from 3.0 to 1.5—a 50% decrease—while preserving subjective motion fidelity without statistical degradation. This work establishes an interpretable, tunable control paradigm for high-fidelity, low-sickness driving simulation.

Technology Category

• Computer Vision: Motion & Tracking
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Driving simulators are increasingly used in research and development. However, simulators often cause motion sickness due to downscaled motion and unscaled veridical visuals. In this paper, a motion cueing algorithm is proposed that reduces motion sickness as predicted by the subjective vertical conflict (SVC) model using model predictive control (MPC). Both sensory conflict and specific force errors are penalised in the cost function, allowing the algorithm to jointly optimise fidelity and comfort. Human-in-the-loop experiments were conducted to compare four simulator motion settings: two variations of our MPC-based algorithm, one focused on pure specific force tracking and the second compromising specific force tracking and motion sickness minimisation, as well as reference adaptive washout and no motion cases. The experiments were performed on a hexapod driving simulator with participants exposed to passive driving. Experimental motion sickness results closely matched the sickness model predictions. As predicted by the model, the no motion condition yielded the lowest sickness levels. However, it was rated lowest in terms of fidelity. The compromise solution reduced sickness by over 50% (average MISC level 3 to 1.5) compared to adaptive washout and the algorithm focusing on specific force tracking, without any significant reduction in fidelity rating. The proposed approach for developing MCA that takes into account both the simulator dynamics and time evolution of motion sickness offers a significant advancement in achieving an optimal control of motion sickness and specific force recreation in driving simulators, supporting broader simulator use.

Problem

• Reducing motion sickness in driving simulators caused by downscaled motion
• Optimizing motion cueing algorithms to balance fidelity and user comfort
• Developing MPC-based solutions that minimize subjective vertical conflicts

Innovation

• MPC-based motion cueing algorithm reduces sickness
• Penalizes sensory conflict and force errors jointly
• Optimizes both motion fidelity and user comfort