Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:816-819. doi: 10.1109/EMBC48229.2022.9871979.
The risk for passengers of an automated vehicle to suffer from motion sickness symptoms increases while performing non-driving tasks. Motion sickness, whether at sea, in the air, in a car or in virtual reality, has been studied for years, but the specific motion patterns of different vehicles and the individual physiology of passengers complicate the definition of general applicable models. Technical progress in vehicles, e.g. the development of the chassis or general digitalization, is constantly changing the influences and marginal effects of motion sickness. In recent years, increasing number of investigations concentrated on the influencing factors on motion sickness. However, the relation between emesis and vehicle dynamics itself is predominantly inadequately presented. Therefore, the results can poorly be incorporate in mathematical models of the sensory conflict theory established as leading theory in the research community. In our research, we suggest a method to prepare and present route and driving information to increase the transparency of real-world driving experiments. We used determined position-based spectrograms to simplify the understanding of the provoked acceleration as well as frequency, known as important motion sickness trigger. Standardized use of this method would support review articles about driving experiments and thus support research regarding motion sickness prediction and occurrence in vehicles.
乘客在执行非驾驶任务时，患晕动病症状的风险会增加。多年来，人们一直在研究晕动病，无论是在海上、空中、汽车还是虚拟现实中，但不同车辆的特定运动模式和乘客的个体生理机能使定义通用适用模型变得复杂。车辆技术的进步，例如底盘的发展或全面数字化，不断改变晕动病的影响和边际效应。近年来，越来越多的研究集中在晕动病的影响因素上。然而，呕吐与车辆动力学本身之间的关系主要没有得到充分体现。因此，这些结果在作为研究界主要理论的感觉冲突理论的数学模型中很难得到体现。在我们的研究中，我们建议了一种准备和呈现路线和驾驶信息的方法，以增加真实驾驶实验的透明度。我们使用基于确定位置的频谱图来简化对被激发加速度的理解，以及已知的重要晕动病触发因素频率。这种方法的标准化使用将支持关于驾驶实验的综述文章，从而支持关于车辆中晕动病预测和发生的研究。
