Using an autonomous vehicle fleet instead of owning a car might significantly reduce the number of vehicles in cities, which may have important consequences related to land use and the urban landscape. More information is available about these possibilities; at the same time, much less is known about whether urban residents would accept them. Moreover, the majority of research addressing the preferences of urban residents presents findings on the entire population rather than on its specific sections; thus, scarce information is available about the urban landscape preferences of young people, who are highly exposed to autonomous vehicle-driven future mobility. This study aims to determine how much young city dwellers accept potential specific urban landscape changes triggered by autonomous vehicles. The research applied real-time eye-tracking tests and supplementary questionnaires to a sample of 102 participants. The tests were carried out under laboratory conditions, during which the subjects looked at before/after urban landscape pairs of images that depicted the potential urban landscape and land use changes triggered by the mass adoption of autonomous vehicles. The examination of the total fixation duration, the average fixation duration and the average number of fixations indicates that the “after” images were collectively more appealing to the subjects. An analysis of the reasons behind the eye-tracking results revealed that safety and human-centered design were identified as the most significant factors across various image pairs.

Previous studies have identified trust as one of the key factors in the technology acceptance of autonomous vehicles. As these studies mostly investigated the population in general, little is known about segment-specific differences. Furthermore, the widely used survey methods are less able to capture the deeper forms of trust—which neuroscientific methods are much better suited to capture. The main objective of our research is to study trust as one of the key factors of technology acceptance related to autonomous vehicles by using neuroscientific methods for specific consumer segments. Real-time eye-tracking tests were applied to a sample of 113 participants, combined with a posttest self-report. The tests were carried out under laboratory conditions during which our subjects watched videos recorded with the internal cameras of autonomous vehicles. Based on the fixation count, total fixation duration, and pupil dilation, we empirically verified that the trust level of all five identified segments is relatively low, while the trust level of the “traditional rejecting” segment is the lowest. An increase in trust level can be shown if the subjects receive extra information about the journey. Another important finding is that the self-reported trust level is not always congruent with the eye-tracking analysis results; therefore, combined approaches can lead to greater measurement validity.