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Motion sickness is a physiological condition that negatively impacts a person's comfort and will be an emerging condition in autonomous vehicles without proper countermeasures. The vestibular system plays a key role in the origin of motion sickness. Understanding the susceptibility and (mal) adaptive mechanisms of the highly integrated vestibular system is a prerequisite for the development of countermeasures. We hypothesize a differential association between motion sickness and vestibular function in healthy individuals with and without susceptibility for motion sickness. We quantified vestibular function by measuring the high-frequency vestibulo-ocular reflex (VOR) using video head impulse testing (vHIT) in 17 healthy volunteers before and after a 11 min motion sickness-inducing naturalistic stop-and-go car ride on a test track (Dekra Test Oval, Klettwitz, Germany). The cohort was classified as motion sickness susceptible (n = 11) and non-susceptible (n = 6). Six (out of 11) susceptible participants developed nausea symptoms, while a total of nine participants were free of these symptoms. The VOR gain (1) did not differ significantly between participant groups with (n = 8) and without motion sickness symptoms (n = 9), (2) did not differ significantly in the factor time before and after the car ride, and showed no interaction between symptom groups and time, as indicated by a repeated measures ANOVA (F(1,15) = 2.19, p = 0.16. Bayesian inference confirmed that there was “anecdotal evidence” for equality of gain rather than difference across groups and time (BF10 < 0.77). Our results suggest that individual differences in VOR measures or adaptation to motion sickness provocative stimuli during naturalistic stop-and-go driving cannot predict motion sickness susceptibility or the likelihood of developing motion sickness.

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An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-world scenarios pose a different kind of problem that could not be solved yet. Specifically, in autonomous driving—it would be beneficial if the car could identify non-verbal communication of pedestrians or other drivers, as it is a common way of communication in daily traffic. Automated identification from observation whether pedestrians or other drivers communicate through subtle cues in mimicry is an unsolved problem so far, as intent and other cognitive factors are hard to derive from observation. In contrast, communicating persons usually have clear understanding whether they communicate or not, and such information is represented in their mindsets. This work investigates whether the mental processing of faces can be identified through means of a Passive Brain-Computer Interface (pBCI). This then could be used to support the cars' autonomous interpretation of facial mimicry of pedestrians to identify non-verbal communication. Furthermore, the attentive driver can be utilized as a sensor to improve the context awareness of the car in partly automated driving. This work presents a laboratory study in which a pBCI is calibrated to detect responses of the fusiform gyrus in the electroencephalogram (EEG), reflecting face recognition. Participants were shown pictures from three different categories: faces, abstracts, and houses evoking different responses used to calibrate the pBCI. The resulting classifier could distinguish responses to faces from that evoked by other stimuli with accuracy above 70%, in a single trial. Further analysis of the classification approach and the underlying data identified activation patterns in the EEG that corresponds to face recognition in the fusiform gyrus. The resulting pBCI approach is promising as it shows better-than-random accuracy and is based on relevant and intended brain responses. Future research has to investigate whether it can be transferred from the laboratory to the real world and how it can be implemented into artificial intelligences, as used in autonomous driving.