Biblio

Autonomous, shared, and electric - this is the vision for future transport services that enable both efficient and climate-friendly mobility. The success of such services will crucially depend on their actual use by the population, which is in turn determined by perceptions of their usefulness, ease of use, safety, and attractiveness. The new features even entail some new challenges to users. The authors present methods to identify user needs and potential use barriers early in the process of designing autonomous vehicles systems for public transport, and give examples from their user-centered research methods which can be used to incorporate user needs in the development of advanced systems for public transport.

The automotive domain currently experiences a radical transition towards automation, connectivity and digitalization. This is a cause for major change in human-machine interaction. The research presented here examines 1) company visions of future mobility 2) user's reaction to the first trials of these visions. The data analyses reveal that implementing companies' visions for 2040 requires improvement concerning user acceptance. One way of improving user acceptance is to integrate emotion recognition in manual and automated vehicles. By reacting to users' positive and negative emotions, vehicles can learn to improve driving behavior, communication and to adjust driver assistance accordingly. Therefore, a roadmap for future research in emotion recognition has been developed by interviews with twelve experts in the field. Emotions that they judged to be most relevant to detect include anger, stress and fear, amongst others. Furthermore, ideas on sensors for emotion recognition, potential countermeasures for the negative effects of emotions and additional challenges were collected. The research presented is designed to shape further research directions of in-car emotion recognition.