  Motion Sickness (MS) is an issue of most transportation systems. Several
countermeasures for such problem in cars are proposed in the literature, but
most of them are qualitative, behavioural or involving complex chassis systems.
With the growing interest in self-employed vehicles, the issue of MS may be so
important that it undermines their benefits in terms of increased productivity;
not addressing such issue may limit the users' acceptance reducing the safety
and environmental impact of autonomous vehicles. The present study presents a
novel approach to combine minimal travel time with minimal Motion Sickness
Incidence (MSI) by optimising the speed profile for a given path. Through
simulation, a simple vehicle model is used to compare several strategies
evaluating which of them are effective and which not. The optimisation task is
formulated as a Non-linear Model Predictive Control (NMPC) and a series of
optimisation routines are computed along the path; the strategies are
implemented within the cost function of the NMPC problem evaluating their
performance and if using a numerical MS model is mandatory to get a significant
reduction of the MSI. The results show that not all the cost functions are
effective, but it is possible to reduce MS without modelling its dynamics;
however, the strategy taking into account the current MSI using a MS model
outperforms the other cost functions in term of efficacy and efficiency. Such a
quantitative approach can be used during motion planning in autonomous
vehicles, to suggest an optimal speed in human-driven vehicles and to improve
the comfort for bus-line services or even trains.
