This study explored several anticipatory interfaces as motion sickness mitigation tool in autonomously driving vehicles. The interfaces were designed to lower the mismatch between the expected motion and the sensory input in a moving body, by providing anticipatory information on upcoming vehicle motion. The two proposed interfaces were a Light only interface and a Combined interface featuring both light and sound. The interfaces present information on the direction and magnitude proportional to the upcoming vehicle accelerations, 0.9s before the actual event. The interfaces were tested in separate trials and compared to a Baseline trial. The comparison was done by a mixed-order, within-participants experiment (n=18), performed in the SIMONA Research Simulator, with identical 30-minute driving conditions. To test the interfaces’ effectiveness in the most motion sickness-triggering setting, subjects got a reading task on a handheld tablet. The results were quantified on an 11-point motion sickness scale and subjective survey questions. Improvements in motion sickness and subjective experience were found with both interfaces. However, only the Combined interface trial showed significant improvements compare to the Baseline. Neither of the interfaces was considered intrusive, while only the Combined interface was considered comfortable. The survey also clearly favored the Combined interface in scores on timing cor- rectness, intuitiveness, and subjective cue correctness. Although the Combined interface had the best results, strong individual preferences were reported for both interfaces, a hypothetical sound interface, or no interface. Further research is advised to focus on the Combined interface while considering optional light and/or sound disengagement in line with personal preference.

Autonomous vehicles are becoming increasingly prevalent in society, but the transition from active driver to passive passenger is known to increase the risk and severity of motion sickness. Motion anticipation is a critical factor in this difference, and visual information is known to be a major contributor to motion anticipation. However, the underlying mechanisms of how visual information influences motion sickness remain largely unknown. This study aims to investigate the effect of visual translation on motion sickness development. To accomplish this, a proposal was made to extend an existing motion sickness prediction model with visual translational components. A human-out-of-the-loop experiment was conducted in a moving-base simulator to test the proposed model's reliability and determine whether visual translation influences motion sickness. The experiment simulated an urban ride that featured repetitive accelerating and breaking maneuvers on a straight road. Eighteen participants were subjected to three different visual conditions that varied in the amount of global optical flow, while the simulator's motion was identical. The results suggest that congruent visual translation may lead to a minor, non-significant reduction of motion sickness, while the amount of optical flow does not seem to affect it. The proposed motion sickness model was able to predict a slight decrease in motion sickness for conditions with visual optical flow, as demonstrated by the experiment. Nonetheless, it is still debatable whether congruent visual translation may influence the onset and severity of motion sickness, as the results of this study could not confirm this.

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.

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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. @en