Motion sickness (MS) is known to be a potentially limiting factor for future self-driving vehicles – specifically in regards to occupant comfort and well-being. With this as a consideration comes the desire to accurately measure, track and even predict MS state in real-time. Previous research has considered physiological measurements to measure MS state, although, this is mainly measured after an MS exposure and not throughout exposure(s) to a MS task. A unique contribution of this paper is in the real-time tracking of subjective MS alongside real-time physiological measurements of Electrodermal Activity (EDA) and skin temperature. Data was collected in both simulator-based (controlled) and on-road (naturalistic) studies. 40 participants provided at total of 61 data sets, providing 1603 min of motion sickness data for analysis. This study is in agreement that these measures are related to MS but evidenced a total lack of reliability for these measures at an individual level for both simulator and on-road experimentation. It is likely that other factors, such as environment and emotional state are more impactful on these physiological measures than MS itself. At a cohort level, the applicability of physiological measures is not considered useful for measuring MS accurately or reliably in real-time. Recommendations for further research include a mixed-measures approach to capture other data types (such as subject activity) and to remove contamination of physiological measures from environmental changes.

众所周知，晕车 (MS) 是未来自动驾驶汽车的潜在限制因素——特别是在乘客舒适度和幸福感方面。考虑到这一点，人们希望实时准确地测量、跟踪甚至预测 MS 状态。以前的研究已经考虑了生理测量来测量 MS 状态，尽管这主要是在 MS 暴露之后测量，而不是在暴露于 MS 任务的整个过程中进行测量。本文的一个独特贡献是实时跟踪主观 MS，同时实时测量皮肤电活动 (EDA) 和皮肤温度。在基于模拟器（受控）和道路（自然主义）研究中收集数据。40 名参与者提供了总共 61 个数据集，提供了 1603 分钟的晕动病数据用于分析。本研究同意这些措施与 MS 相关，但证明这些措施在模拟器和道路实验的个人水平上完全缺乏可靠性。其他因素，例如环境和情绪状态，可能比 MS 本身对这些生理指标的影响更大。在队列水平上，生理测量的适用性不被认为对实时准确或可靠地测量 MS 有用。进一步研究的建议包括混合测量方法来捕获其他数据类型（例如受试者活动）并消除环境变化对生理测量的污染。