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.

翻译：驾驶模拟器在研究与开发中的应用日益广泛。然而，由于运动尺度缩减与视觉信息未按比例调整，模拟器常引发晕动症。本文提出一种运动提示算法，该算法基于模型预测控制（MPC）框架，依据主观垂直冲突（SVC）模型的预测来降低晕动症。算法在代价函数中同时惩罚感觉冲突与比力误差，从而实现对保真度与舒适度的联合优化。研究通过人在环实验比较了四种模拟器运动设置：我们提出的基于MPC算法的两种变体（一种专注于纯比力跟踪，另一种在比力跟踪与晕动症最小化之间折衷），以及参考的自适应洗出算法和无运动条件。实验在六自由度驾驶模拟器上进行，参与者体验被动驾驶场景。实验测得的晕动症结果与晕动症模型预测高度吻合。如模型所预测，无运动条件下的晕动程度最低，但其保真度评分也最低。折衷方案相较于自适应洗出算法及专注于比力跟踪的算法，将晕动程度降低了50%以上（平均MISC等级从3降至1.5），且未导致保真度评分显著下降。所提出的运动提示算法开发方法同时考虑了模拟器动力学与晕动症的时间演化特性，为实现驾驶模拟器中晕动症控制与比力再现的最优调控提供了重要进展，有助于推动模拟器的更广泛应用。