  Passengers of level 3-5 autonomous personal mobility vehicles (APMV) 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, especially when frequently avoiding pedestrians or obstacles in
shared spaces. Many studies have been conducted to build countermeasures, of
which various computational motion sickness models have been developed. Among
them, models based on subjective vertical conflict (SVC) theory, which
describes vertical changes in direction sensed by human sensory organs v.s.
those expected by the central nervous system, have been actively developed.
However, no current computational model can integrate visual vertical
information with vestibular sensations.
  We proposed a 6 DoF SVC-VV model which added a visually perceived vertical
block into a conventional 6 DoF SVC model to predict visual vertical directions
from image data simulating the visual input of a human.
  In a driving experiment, 27 participants experienced an APMV with two visual
conditions: looking ahead (LAD) and working with a tablet device (WAD). We
verified that passengers got motion sickness while riding the APMV, and the
symptom were severer when especially working on it, by simulating the frequent
pedestrian avoidance scenarios of the APMV in the experiment. In addition, the
results of the experiment demonstrated that the proposed 6 DoF SVC-VV model
could describe the increased motion sickness experienced when the visual
vertical and gravitational acceleration directions were different.
