Reducing Motion Sickness by Manipulating an Autonomous Vehicle's Accelerations

Rowenna Wijlens*, Marinus M. van Paassen, Max Mulder, Atsushi Takamatsu, Mitsuhiro Makita, Takahiro Wada

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

4 Citations (Scopus)
80 Downloads (Pure)


Without intervention the widespread adoption of autonomous vehicles could be compromised by an increased incidence of motion sickness compared to conventional cars. To investigate whether passengers' motion sickness can be reduced by manipulating an autonomous vehicle's accelerations on a fixed route without altering the travel time, a human-out-of-the-loop experiment was performed in the SIMONA Research Simulator at Delft University of Technology. The experiment consisted of two different driving conditions, in which an identical 22-km road including 52 curves was travelled in 30 minutes. Condition 1 comprised larger longitudinal, but smaller lateral, acceleration values compared to Condition 2. Experimental results suggested that Condition 1 resulted in more severe motion sickness than Condition 2, with fitted learning curves providing final MIsery SCale scores of 1.19 vs. 0.80. A similar relative difference between the two conditions had been predicted by the 6-DOF Subjective Vertical Conflict model. Hence, this model has the potential to, once further developed, support the design of autonomous vehicles by reducing the need to perform costly, time-consuming experiments.

Original languageEnglish
Pages (from-to)132-137
Number of pages6
Issue number29
Publication statusPublished - 2022
Event15th IFAC Symposium on Analysis, Design and Evaluation of Human Machine Systems, HMS 2022 - San Jose, United States
Duration: 12 Sept 202215 Sept 2022


  • autonomous vehicles
  • driving
  • mitigation
  • modeling
  • motion sickness


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