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SimPath: Mitigating Motion Sickness in In-vehicle Infotainment Systems via Driving Condition Adaptation

Jinghao Huang, Siqi Yao, Yu Zhang

TL;DR

SimPath addresses motion sickness (MS) in IVIS by adapting visual content to driving dynamics, linking visual motion to vehicle acceleration through a function $g(a)$ and real-time sensor data. Two real-road studies show that standard driving footage can reduce MS, while SimPath alone may not, due to latency and perceptual misalignment; adding anticipatory prompts and a third-person car model can further reduce MS but may distract from IVIS tasks, affecting efficiency. The work offers design guidelines and theoretical discussion on balancing passenger comfort with task performance, emphasizing perceptual alignment and attention management for future autonomous-vehicle IVIS. Overall, SimPath demonstrates potential for MS mitigation in IVIS while highlighting trade-offs between comfort and engagement in real-world driving contexts.

Abstract

The problem of Motion Sickness (MS) among passengers significantly impacts the comfort and efficiency of In-Vehicle Infotainment Systems (IVIS) use. In this study, we innovatively designed SimPath, a visual design to effectively mitigate passengers' MS and boost their efficiency of using IVIS during driving. The study focuses on the problem of irregular motion conditions frequently encountered during actual driving. To validate the efficacy of this approach, two sets of real - vehicle experiments were carried out in real driving scenarios. The results demonstrate that this approach significantly reduces passenger's MS level to a certain extent. However, due to divided attention from visual content, it does not directly improve the IVIS efficiency. In conclusion, this study offers crucial insights for the design of a more intelligent and user friendly IVIS, based on the discussion of the principle, providing strong theoretical support and practical guidance for the development of future IVIS in autonomous vehicles.

SimPath: Mitigating Motion Sickness in In-vehicle Infotainment Systems via Driving Condition Adaptation

TL;DR

SimPath addresses motion sickness (MS) in IVIS by adapting visual content to driving dynamics, linking visual motion to vehicle acceleration through a function and real-time sensor data. Two real-road studies show that standard driving footage can reduce MS, while SimPath alone may not, due to latency and perceptual misalignment; adding anticipatory prompts and a third-person car model can further reduce MS but may distract from IVIS tasks, affecting efficiency. The work offers design guidelines and theoretical discussion on balancing passenger comfort with task performance, emphasizing perceptual alignment and attention management for future autonomous-vehicle IVIS. Overall, SimPath demonstrates potential for MS mitigation in IVIS while highlighting trade-offs between comfort and engagement in real-world driving contexts.

Abstract

The problem of Motion Sickness (MS) among passengers significantly impacts the comfort and efficiency of In-Vehicle Infotainment Systems (IVIS) use. In this study, we innovatively designed SimPath, a visual design to effectively mitigate passengers' MS and boost their efficiency of using IVIS during driving. The study focuses on the problem of irregular motion conditions frequently encountered during actual driving. To validate the efficacy of this approach, two sets of real - vehicle experiments were carried out in real driving scenarios. The results demonstrate that this approach significantly reduces passenger's MS level to a certain extent. However, due to divided attention from visual content, it does not directly improve the IVIS efficiency. In conclusion, this study offers crucial insights for the design of a more intelligent and user friendly IVIS, based on the discussion of the principle, providing strong theoretical support and practical guidance for the development of future IVIS in autonomous vehicles.

Paper Structure

This paper contains 36 sections, 2 equations, 12 figures.

Table of Contents

  1. Introduction
  2. Method
  3. Study-1-virtual scence
  4. Experimental design
  5. Experimental procedures
  6. Subject selection
  7. Experimental process
  8. Experiment equipment and road condition
  9. Experimental Results and Analysis
  10. MS Level Analysis
  11. Electrocardiogram and Electrodermal Activity
  12. Task Performance Analysis
  13. Discussion
  14. MS Sensations
  15. Task Performance
  16. ...and 21 more sections

Figures (12)

  • Figure 1: Graphical Abstract: Adaptive Visualization of Driving Dynamics to Mitigate Motion Sickness in IVIS
  • Figure 2: (a). Function graph of g(a); (b). Example of the change of point $P1$
  • Figure 3: Images of Simpath under different driving conditions
  • Figure 4: (a). Display interface in three conditions; (b). Display interface in screen for participants; (c). Method for participants to read the article and report their MS value during the experiment; (d). Road condition; (e). Interior layout in the vehicle; (f). Pictures of the vehicle
  • Figure 5: (a). MS Value across the three conditions (Pre-SSQ and Post-SSQ); (b). Average MS value(Likert scale) across the three conditions; (c). Reading speed across the three conditions; (d). Average HR, RR interval, LF/HF and SCL across the three conditions; Significance levels are indicated as *p < 0.05, **p < 0.01, and ***p < 0.001; All box plots in this article was drawn using ChiPlot (https://www.chiplot.online/)
  • ...and 7 more figures