Model-based visual cue design for mitigating motion sickness: development, theoretical analysis, and experimental validation.

Autonomous vehicles are expected to exacerbate motion sickness (MS), since occupants may engage more in non-driving-related activities. Due to their availability and user experience, presenting visual cues of vehicle motion has become an effective MS mitigation approach. However, approaches to designing visual cues rely on empirical methodology and subjective feedback, while overlooking the mechanism link between cue design and MS mitigation effects. Considering both user-friendliness and systematic optimisation, a model-based visual cue design framework is proposed, adopting a computational model capable of quantifying and interpreting the effectiveness of visual cues on MS. Taking a dot-matrix visual cue as an example, driving simulator experiments were conducted, showing that the design framework provides objective and model-informed insights for cue evaluation and refinement. This research may work as a foundation for data-driven design of MS mitigation strategies in autonomous vehicles.