Optimal Trajectory Planning for Mitigated Motion Sickness: Simulator Study Assessment

Abstract

In the transition from partial to high automation, occupants will no longer be actively involved in driving. This will allow the use of travel time for work or leisure, where high comfort levels preventing motion sickness are required. In this paper, an optimal trajectory planning algorithm is presented in order to minimise motion sickness in automated vehicles. A predefined path is provided as an input to the algorithm, to generate an optimal path with limited lateral deviation and the corresponding optimal velocity profile, for the minimisation of motion sickness. An optimal control problem is formulated with a cost function combining both motion sickness and travel time. For a sickening curvy road, the algorithm reduced the motion sickness dose value (MSDV) up to 52% depending on the allowed lateral deviation and the weighting on travel time. The efficacy of the proposed algorithm has been evaluated via human-in-the-loop experiments using a moving-base driving simulator. Motion cueing parameters were selected to optimally transmit the sickening stimuli resulting in close to full vibration transmission above 0.2 Hz. During the experiment, the participants were asked to rate their experience based on the standard MIsery SCore ratings. According to these, sickness levels were reduced on average by 65% with reduced motion sickness in all 16 participants.

Mitigating Motion Sickness in Autonomous Vehicles for Improved Passenger Comfort

“…The challenge of resolving MS is shaping up to be especially relevant and important in both passenger-driven vehicles and autonomous vehicles. Considering the rising popularity of autonomous vehicles, MS will hinder the seam-less integration of self-driving transport into daily commutes [28], [29]. Autonomous vehicles should afford passengers the freedom to engage in productive activities during their commute.…”