Urban Air Mobility (UAM) has lately emerged as a time-saving mode of air transportation in congested urban areas. However, several challenges to the commercialization and adoption of UAM vehicles exist, such as licensing and restrictions, security, and construction infrastructure. Besides these, public perception is one of the significant aspects of easing psychological stress and representing users’ needs. A survey study was carried out to understand the public perception of UAM and to comprehend how the public perceives and expects UAM aircraft in terms of user requirement analysis as part of human-centered design. To achieve this, a total of 2,847 valid data sets were analyzed. A preliminary data analysis revealed the general level of awareness of UAM machines, expected costs and values for specific scenarios, perceived advantages of UAM vehicles, and overall opinion, along with distinctions based on demographic information such as age groups, residential areas, and income levels. Future research topics might include in-depth data analysis and subsequent user surveys to discover underlying requirements.

In conditional automated driving, drivers may be required starting manual driving from automated driving mode after take-over request (TOR). The objective of the study was to investigate different TOR features for drivers to engage in manual driving effectively in terms of reaction time, preference, and situation awareness (SA). Five TOR features, including four features using countdown, were designed and evaluated, consisted of combinations of different modalities and codes. Results revealed the use of non-verbal sound cue (beep) yielded shorter reaction time while participants preferred verbal sound cue (speech). Drivers' SA was not different for TOR features, but the level of SA was affected by different aspects of SA. The results may provide insights into designing multimodal TOR along with drivers' behavior during take-over tasks.