Communication between pedestrians and drivers partially relies on nonverbal communication methods such as eye-contact and gestures. With the transition from manually driven vehicles to automated vehicles (AVs), pedestrians could lose the ability to communicate their intention to the driver. This study investigated the use of hand gestures as a new form of communication from the pedestrian to the AV. Twenty-six participants participated in a Virtual Reality (VR) experiment, in which they wore an Oculus Rift to interacted with AVs in a virtual environment. The movement of the participants was recorded and visualized through the use of a Xsens Link motion tracking suit, which provided the research with data about the hand gesture usage. The main independent variable of this study was the permission for the participant to use hand gestures to try to make the AV yield. The hand gesture increased the probability of the AV stopping for the participant. The second independent variable was the response of the AV through a message on an external-Human Machine Interface (eHMI). The participants went through four different scenarios. Therefore, both one-way communication and two-way communication were investigated in the same experiment. The participants were given the freedom to decide if they wanted to use the hand gesture. Aside from the hand gesture, the participants were asked to perform a forward step at the moment they felt safe to cross the road in the virtual environment, without actual crossing. Alongside the gathered data on movement of the participants, the research also included data gathered from questionnaires in which the participants were asked about their feeling of safety, assurance of being seen by the AV, the effect that the lack of eye-contact had on their decision making, difficulty predicting the behaviour of the AV, and their trust in communication involving AVs and hand gestures. The research found that the participants used hand gestures to communicate crossing intent to the AV around 80% of the time. The ability to use hand gestures did not improve the feeling of safety significantly, and made it more difficult for the participants to predict the behaviour of the AV. The results of the subjective measurements did show positive results for the hand gesture in combination with responses from the AV by the eHMI, as well as for the eHMI alone. It is concluded that participants were willing to use the hand gesture, and that the hand gesture only increased the subjective feeling of safety if the AV responds to the hand gesture via an eHMI.

Self-driving cars is considered the next major step in the automotive industry and with automation in passenger vehicles, the driver can benefit from the freed up time for leisure or work, as he or she becomes the passenger. However, this is only possible if the drivers are comfortable during automated driving. The major issue here, is that the susceptibility of motion sickness (MS) increases significantly when the driver does not have his or her eyes on road, this susceptibility needs to be minimized. However, motion sickness is not clearly understood and assessing it has been traditionally done qualitatively with questionnaires. Because of the highly individual and subjective nature of motion sickness and its symptoms, it is hard to quantify it accurately by questionnaires. To minimize and prevent motion sickness, it is beneficial to measure it quantitatively in different ways in addition to the traditional questionnaire.
This thesis explores and investigates the use of physiological measurements, ECG and GSR, to relate motion sickness in a realistic driving experiment.
Hence, a road test is conducted for this study with a Toyota Prius on a closed road. A slalom course was driven with a speed 25 km/h to reach lateral accelerations up to 0.4G with a lateral frequency of 0.175 Hz. This frequency and velocity has been chosen, because it is known that people are the most sensitive for MS of frequencies near 0.2 Hz and this velocity reflects urban driving. 23 participants took part of the experiment and had their ECG, GSR and their MISC (Misery scale, an illness rating) recorded during the drive. The experiment lasted until MISC rating 7 (=medium nausea) or either 30 minutes was completed. The ECG (HR, LF/HF ratio) and GSR (skin conductance level SCL, skin conductance response SCR) recordings were then compared to the MISC ratings to see if there was a significant difference between the participants who got sick and stopped at MISC 7 (sensitive) and the participants who did not get sick (non-sensitive).
The results show little support correlating motion sickness or even to distinguish sensitive and non-sensitive groups with HR, HRV or GSR data. It might be beneficial to categorize people into different sensitivity profiles for MS susceptibility to make HR information more useful as there is too much of individual differences. Currently, there were few to none road tests done regarding motion sickness. It appears that physiological measurements for predicting MS in vehicles are not as straightforward and do not translate well from other types of laboratory tests to realistic road tests. Not to mention that HRV has become a controversial metric in the recent decade that might need to be re-evaluated for use.