Virtual reality-induced motion sickness under autonomous vehicle driving conditions: EEG-based recognition and ANOVA analysis of various driving modes.

Abstract

Motion sickness induced by autonomous driving technology poses a new challenge to the emerging sustainable transportation systems. This study investigates the association between motion sickness in autonomous driving and electroencephalogram (EEG) signals under three laboratory-based simulated scenarios: manual driving, resting, and autonomous driving. EEG data were recorded from participants in each mode, alongside the collection of motion sickness symptoms through questionnaires. Data analysis and exploration were conducted to explore the relationship between autonomous driving-induced motion sickness and EEG signals. The results indicate a significantly higher probability of motion sickness among passengers in autonomous driving mode than in manual one. Across different driving modes, a correlation was observed between the amplitude and latency of N200 and P300 event-related potentials (ERPs) in the Go/Nogo paradigm, reflecting response inhibition and the occurrence of motion sickness. Temporal analysis of EEG signals revealed significant differences in the Kolmogorov complexity values at Cz, Fz, and Pz channels, suggesting the potential use of EEG-based detection of motion sickness. Frequency domain analysis indicated increased activity in alpha and gamma waves and decreased activity in beta waves following the onset of motion sickness during autonomous driving. Distinct changes were observed in the electrocortical topography of N200 and P300 components in autonomous driving through event-related potential waveforms and topographic maps. These findings provide new insights into the neural mechanisms of motion sickness in autonomous driving and offer guidance for future intervention methods and improvements in the design of autonomous driving systems, thereby promoting their sustainability and safety.