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.

This paper presents the results of a training and retention experiment conducted to objectively and quantitatively evaluate the acquisition, decay, and retention of skill-based manual control behavior in a compensatory dual-axis roll and pitch attitude tracking task. In this study, thirty-eight fully task-naive participants were trained in a fixed-base setting in the Human-Machine Interaction Laboratory at Delft University of Technology and subsequently divided into three matched groups based on their training performance and control behavior. Performance of the first group was re-evaluated after a period of non-practice of six months, whereas the second group was retested at both three and six months after training, and skill retention of the third group was measured after two, four, and six months. The goal of the experiment was to model the decay curve of skill-based manual control behavior and to determine the re-acquisition rate of lost skills compared to their initial acquisition rate. To explicitly quantify changes in manual control skills, learning curve models were fitted to metrics of task performance and control activity. The results suggest that control skills decay following a negatively accelerating decay curve and that lost skills are re-acquired at a higher rate than their initial development rate.