With the introduction of Automated Vehicles (AVs), new potential traffic situations could emerge on the roads. Although several studies on AVs have been conducted, most of these studies focus on the AV technology or on the driver, while there is less emphasis on how these AVs could interact and communicate with other road users. The interactions of vulnerable road users with AVs versus Conventional Vehicles (CVs) or the amount of trust in AVs versus in CVs, can be different. Due to the high fatality and injury rates and the large number of cyclists in the Netherlands, this research will focus on cyclists. Currently, there is little knowledge about cyclists’ behaviour, interacting with these AVs. The main goal of this study is to investigate whether the crossing behaviour of cyclists differs when interacting with an AV compared to a CV. An experiment in Virtual Reality has been performed to study crossing decisions of participants when interacting with both AVs and CVs. Multiple scenarios were shown to the participants, who could either choose to: ‘continue cycling’, ‘cycle faster’ or ‘slow down’. A mixed model with repeated measures was estimated to identify which variables influence participants’ crossing decisions. The included variables were: vehicle type, gap distance, crossing priority, risk taking, stated trust and the interaction between the vehicle type and stated trust. The results show that there is no significant difference in the crossing decision between the two types of vehicles when the total group of participants is considered. However, when participants are divided into groups based on their stated trust in AVs, significant statistical differences were observed in the crossing behaviour between the two types of vehicles. Participants who have more trust in AVs compared to CVs, crossed more often in front of AVs. The ones who trusted AVs less, choose to slow down more often in front of the AVs. The awareness of the type of vehicle increased their preference based on their stated trust even more and made the differences in the crossing behaviour between the groups bigger. This study shows that cyclists adapt their behaviour when interacting with AVs, based on their amount of trust in AV technology. These findings are important to reminisce for the continuous developing of AV technology. Furthermore, additional research can build upon this study to formulate a policy on AVs. To see how the behaviour of cyclists will evolve in the future, more research about the learning effects of interacting with AVs is necessary.

This thesis researches the effect of smart mobility on parking space. To what extent will space become available and how can this be used for the restructering challenges within the urban enviroment?

Automated vehicles (AVs) are being developed by several companies and research groups worldwide. The implicit communication (e.g., eye contact) of the vehicle or driver seems to play an important role in the expected behaviour of vehicles. It is possible that vulnerable road users (VRUs) are less able to estimate the intention of vehicles (e.g., deceleration, braking behaviour and stopping distance) when this type of communication changes or disappears when AVs are introduced. An external human-machine interface (eHMI) (i.e., a display showing when it is safe to cross) can be introduced to overcome the lack of implicit communication between driver and pedestrian. The goal of this study was to examine the effects of eHMIs of AVs on the crossing behaviour of VRUs.
A virtual reality (VR) simulation was set up, where the participant was standing on the pavement on a two-lane two-way road in a European setting. The yielding behaviour (yielding, non yielding), the type of vehicle (small, medium, large), the type of eHMI and the timing of an eHMI were varied in a within-subject design (N = 28). Four types of eHMIs were implemented, which all consisted of a screen in the front of the vehicle; with the four eHMIs being 1) the Frontal braking lights, 2) a Knightrider animation, 3) a Smiley and 4) a Text showing ‘WALK’ was shown when it was safe to cross.When it was not safe to cross, the Frontal braking lights were turned off, the Knightrider animation was not shown, the Smiley was neutral and the Text showed ‘DON’T WALK’.
Results showed that the presence of an eHMI made participants significantly feel safer when trying to cross. This was measured by measuring the total time that participants indicated they felt safe to cross using a remote control on which participants could press a button. The total time ratio that participants pressed the button when vehicles were yielding was 0.655, 0.743, 0.747, 0.751, and 0.765 for the baseline, Frontal braking lights, Knightrider, Smiley, and Text, respectively. Thus, participants felt safer to cross when an eHMI was present compared to when no eHMI was present. Secondly, the vehicle size was found to play a role in the total time that participants felt safe to cross. Specifically, the total time ratio was 0.746 for a Smart fortwo, 0.732 for a BMW z4, and 0.725 for a Ford f150 in the yielding cases. The total time ratio was 0.206 for a Smart fortwo, 0.190 for a BMW z4, and 0.156 for a Ford f150 in the non-yielding cases. Furthermore, the distance at which an eHMI changes state (e.g., ‘WALK’/‘DON’T WALK’) was found to play a role in the total time that participants felt safe to cross. The total time ratio was 0.796 for 50 m, 0.761 for 35 m, 0.698 for 20 m and 0.655 for the baseline. Participants reported in a post-experiment questionnaire that the Text eHMI was least ambiguous.
Future research can focus on which aspects improve AV-VRU interaction, so that eHMIs can be optimised. Now merely screens in front of the vehicle were tested; other techniques (e.g., projected pedestrian crossings) could yield different results. Differences between research methods can be examined as well, as this study showed differences in revealed behaviour in the simulation and conscious stated preferences of people in the questionnaires.