With the advancements in automated driving, there is concern that motion sickness will increase as non-driving-related tasks increase. Therefore, techniques to reduce motion sickness have drawn much attention. Research studies have attempted to estimate motion sickness using computational models for controlling it. Among them, a computational model for estimating motion sickness incidence (MSI) with visual information as input based on subjective vertical conflict theories was developed. In addition, some studies attempt to mitigate motion sickness by controlling visual information. In particular, it has been confirmed that motion sickness is suppressed by matching head movement and visual information. However, there has been no research on optimal visual information control that suppresses motion sickness in vehicles by utilizing mathematical models. We, therefore, propose a method for generating optimal visual information to suppress motion sickness caused from vehicle motion by utilizing a motion sickness model with vestibular and visual inputs. To confirm the effectiveness of the proposed method, we investigated changes in the motion sickness experienced by the participants according to the visual information displayed on the head-mounted display. The experimental results suggested that the proposed method mitigates the motion sickness of the participants.

翻译：随着自动驾驶技术的发展，非驾驶相关任务的增加可能导致运动晕动症加剧，因此减轻运动晕动症的技术备受关注。已有研究尝试利用计算模型估计运动晕动症以实现控制。其中，基于主观垂直冲突理论，以视觉信息为输入开发了用于估计运动晕动症发生率的计算模型。此外，部分研究试图通过控制视觉信息来缓解运动晕动症，特别是已证实通过匹配头部运动与视觉信息可抑制运动晕动症。然而，目前尚无研究利用数学模型控制车辆中的最优视觉信息以抑制运动晕动症。为此，我们提出一种利用前庭与视觉输入的运动晕动症模型生成最优视觉信息的方法，以抑制车辆运动引发的运动晕动症。为验证所提方法的有效性，我们研究了根据头戴式显示器显示的视觉信息，受试者运动晕动症体验的变化。实验结果表明，所提方法能够缓解受试者的运动晕动症。