# Motion Sickness Modeling with Visual Vertical Estimation and Its Application to Autonomous Personal Mobility Vehicles

arXiv: https://arxiv.org/abs/2202.06299 · HTML: https://mlsanity.com/paper/2202.06299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Autonomous Vehicles Drive into Shared Spaces: eHMI Design Concept Focusing on Vulnerable Road Users](https://mlsanity.com/paper/2104.06916/) (2021)
- [Can Passenger's Active Head Tilt Decrease The Severity of Carsickness? - Effect of Head Tilt on Severity of Motion Sickness in a Lateral Acceleration Environment](https://mlsanity.com/paper/1504.08255/) (2015)
- [Analysis of Driver's Head Tilt Using a Mathematical Model of Motion Sickness](https://mlsanity.com/paper/1711.00747/) (2017)
- [What Is the Gaze Behavior of Pedestrians in Interactions with an Automated Vehicle When They Do Not Understand Its Intentions?](https://mlsanity.com/paper/2001.01340/) (2020)

## Abstract (arXiv)

Passengers (drivers) of level 3-5 autonomous personal mobility vehicles (APMV) and cars can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Many of these are based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs vs. those expected by the central nervous system. Such models are expected to be applied to autonomous driving scenarios. However, no current computational model can integrate visual vertical information with vestibular sensations. We proposed a 6 DoF SVC-VV model which add a visually perceived vertical block into a conventional six-degrees-of-freedom SVC model to predict VV directions from image data simulating the visual input of a human. Hence, a simple image-based VV estimation method is proposed. As the validation of the proposed model, this paper focuses on describing the fact that the motion sickness increases as a passenger reads a book while using an AMPV, assuming that visual vertical (VV) plays an important role. In the static experiment, it is demonstrated that the estimated VV by the proposed method accurately described the gravitational acceleration direction with a low mean absolute deviation. In addition, the results of the driving experiment using an APMV demonstrated that the proposed 6 DoF SVC-VV model could describe that the increased motion sickness experienced when the VV and gravitational acceleration directions were different.