学术文献库

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.