Motion Sickness (MS) remains a paramount concern in the evolving landscape of autonomous vehicles. As automation continues to reshape the passenger-vehicle interaction paradigm, the proliferation of diverse in-vehicle systems empowers passengers to disengage from the driving task. However, this new paradigm comes with the potential cost of exacerbating MS inside the car.In this work, we intended to: (1) present and review available countermeasures to prevent or mitigate MS, found in the literature, that could also be implemented inside a car; (2) identify current trends and gaps in countermeasures to MS; and (3) suggest future avenues of research for potential use-cases aiming to mitigate MS in autonomous driving.Through a comprehensive review of 65 publications, spanning from 2009 to 2023, we have organized existing literature into three distinctive categories and ten subcategories: (1) Vehicle-Centric Adaptation (Adaptation to Surface, Best Route ad Driving Style Adaptation), (2) In-Car Design and Environment (Design Guidelines for Seats, for Displays, for Windows and Climate Control), and (3) Sensory Cues (Visual, Haptic Audio and Olfactory Cues).Our findings suggest that Visual Cues and Motion Planning are the two strongest trends in MS countermeasures. In contrast, Olfactory Cues are the least studied approach. Our results also substantiate the viability of multimodal approaches as a promising solution for passengers in autonomous vehicles. The simultaneous application of various countermeasures might hold potential in mitigating MS effectively.As autonomous vehicles advance, these findings offer a strong basis for future research to decrease passenger's motion sickness and improve their well-being, safety, and comfort inside the car.


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    Title :

    Motion sickness countermeasures for autonomous driving: Trends and future directions


    Contributors:


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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