Abstract
Motion sickness is a significant research topic that can impede the widespread adoption of Internal combustion vehicles (ICV), electric vehicles (EV), and automated vehicles (AV). As the technologies for EVs and AVs continue to advance, Numerous studies have been conducted to investigate motion sickness in vehicle environments considering more detailed driving contexts. Thus, this paper focuses on trends and findings related to methods, strategies, and theories for detecting, preventing, or mitigating motion sickness in vehicles. A total of 52 articles published within the last five years were reviewed to examine the overall trends in motion sickness measurement and evaluation. The results of this study are expected to provide a foundation for the development of motion sickness prevention and mitigation functions, thereby improving the driving experience for users by making it safer and more comfortable.
Research on mitigating motion sickness, especially in the context of transport, has been a focal area of study. As automation vehicle (AV) technology evolves, attention has been shifted to addressing motion sickness in these vehicles, given the transition of users from drivers to passengers. Recognizing the changing dynamics from internal combustion vehicles (ICV), electric vehicles (EV), to AVs, there's an urgent need to develop methods or strategies to close the gap between the unexpected experiences in modern vehicles and the roles users are accustomed to, while also minimizing motion sickness. This paper aims to build upon Iskander's (2019) motion sickness resilient framework in light of the recent advancements such as virtual reality technology used in motion sickness experiments (Winkel, 2021), motion base simulators developed to induce motion sickness (Kuiper, 2019), and machine learning and deep learning techniques used in motion sickness studies (Zheng, 2023). In addition, other works have proposed methods, strategies, and theories to detect, prevent, or mitigate motion sickness.
Evaluating motion sickness in vehicles has been explored through both subjective and objective approaches. Studies have investigated the relationship between motion sickness levels and subjective comfort ratings (Winkel et al., 2022) and between unpleasantness and symptomatology based on subjective reports (Reuten et al., 2021). Simultaneously, objective physiological and motion measurements have been correlated with subjective motion sickness levels (Curry et al., 2020; Irmak, 2020). Individual dynamics in motion sickness evaluation have also been emphasized (Chang et al., 2021). Previous mathematical models on motion sickness, including the 6DOF-SVC (6 degree of freedom – Subjective Vertical Conflict) Model (Buchhet, 2022) and Oman's nausea model (Irmak; Pool; Happee, 2021), have been validated for their applicability in realistic driving scenarios. To conduct an exhaustive analysis of research papers published within the past five years, this study adopts a systematic approach comprising of several steps such as keyword selection, defining search scope and conditions, establishing inclusion/exclusion criteria, expert assessment, and full-text screening.
This literature review (Iskander, 2019) explores three primary streams of studies related to motion sickness (MS) in vehicle environments: research on factors affecting MS, development of prediction models for the severity of MS, and countermeasure research. After the initial search and review of 21 papers, 58 papers were finally selected and analyzed in depth, involving surveys, real-vehicle driving experiments, physiological measures, and studies developing MS prediction and evaluation models.
The selected studies are based on three main theories that explain motion sickness: sensory conflict theory, evolutionary theory, and posture instability theory. Each study had a specific set of participants, and detailed data such as driving experience, MS sensitivity, and group designations were collected (Bae, 2019; Smyth, 2021; El Hamdani, 2022). The severity of MS was measured using real vehicles, simulators, and various data measurement devices and techniques (Henry, 2022; Jones, 2019; Li, 2022; Le, 2020).
The experiments designed in the reviewed studies featured independent variables, such as participants' gender, tasks, and seating positions, as well as dependent variables, including rating scales or surveys, physiological data, and motion dynamic data. Some studies used single-factor scales like MIsery Scale (MISC) (El Hamdani et al., 2022), while others utilized multi-factor scales, such as the Motion Sickness Assessment Questionnaire (MSAQ), and the Fast Motion Sickness (FMS) Scale (de Winkel, 2021; Chang, 2021; Salter, 2019).
Furthermore, the experiments took into account environmental, driving scenarios, and procedure designs (Jones, 2019; Xuan et al., 2021; Kuiper, 2018; Li and Chen, 2022; Chang, 2021). The experimental conditions included various tasks and scenarios such as driving, reading, visual search, and text entry tasks. Data analysis approaches included statistical validation, use of time series data and machine learning algorithms, and data generation related to the degree of motion sickness via simulations (Li, 2022).
Countermeasures for MS in the studies can be categorized into two main areas: optimizing driving style and personalized measures. The former involved modifying acceleration profiles, exposure time, and lane position of the vehicle (Jones, 2019; Yanggu, 2023), while the latter examined the effects of seat arrangement, head-tilt, motion cue, and galvanic cutaneous stimulation on MS (Salter, 2019; Li, 2022; Kuiper, 2018; Gálvez-García et al., 2020).
In conclusion, the study highlights the most recent trends in research on motion sickness in vehicle environments, based on a comprehensive review of 58 papers. The findings contribute to the development of MS prevention and mitigation measures, thereby enhancing the car experience from a user-centered perspective.