We have performed explorative investigations into the socio-technological prerequisites and implications of a personal aerial transportation system. Four focus group interview performed in different European countries have shed light on social expectation towards such a system and its potential role for future mobility.
We found that opinions amongst potential users regarding suitable levels of automation varied and mainly depended on the purpose of the journey. For commuting, a fully autonomous mode was regarded as desirable. However, several fundamental questions remain that relate to technical issues such as energy supply and noise pollution. From the user's perspective, the integration of PAV traffic into the existing transport infrastructures and architecture of European cities will need to be investigated in more detail since existing data are sparse and case-specific.

To make flying a PAV as easy as driving a car, it is necessary to develop human-machine interfaces (HMI) that are intuitive and sensitive to human competencies. We have developed novel multi-sensory HMIs by combining highway-in-the-sky displays with haptic control guidance. These have been used to investigate how pilots respond to guidance forces from various haptic algorithms.
We have also evaluated the human factors associated with PAV flight (e.g., cognitive workload) by employing non-obtrusive real-time methods such as gaze tracking, electro-dermal responses, heart-based measures and electro-encephalography (EEG) to estimate the perceived difficulty of a control tasks.

When many PAVs share the same airspace, a reactive strategy is required to ensure collision-free navigation. We have developed collision avoidance strategies inspired by human crowd behaviour that are able to ensure collision-free navigation and that take passenger comfort into consideration. Not only did we show that in simulation our approach is suitable for environments with up to 140 PAVs per cubic kilometre (more than 3 times originally planned) but also that our approach can be implemented in real-time conditions on unmanned flying vehicles.
We also studied solutions for automatic navigation by means of video data. We targeted three navigation tasks: 1) automatic detection of collisions course and general flight patterns, 2) automatic landing place assessment in man-made environments, and 3) automatic geographic localisation in large environments. We have developed novel computer visions methods that take into account geometry, temporal changes and appearance of the environment gathered from image data. Our techniques are focused on efficiency for on-board deployment. Combined, these methods provide a new set of algorithms that bring intelligent assistance for manned and unmanned aircraft navigation.