  Recent advances in Automated vehicle (AV) technology and micromobility
devices promise a transformational change in the future of mobility usage.
These advances also pose challenges concerning human-AV interactions. To ensure
the smooth adoption of these new mobilities, it is essential to assess how past
experiences and perceptions of social interactions by people may impact the
interactions with AV mobility. This research identifies and estimates an
individual's wellbeing based on their actions, prior experiences, social
interaction perceptions, and dyadic interactions with other road users. An
online video-based user study was designed, and responses from 300 participants
were collected and analyzed to investigate the impact on individual wellbeing.
A machine learning model was designed to predict the change in wellbeing. An
optimal policy based on the model allows informed AV actions toward its
yielding behavior with other road users to enhance users' wellbeing. The
findings from this study have broader implications for creating human-aware
systems by creating policies that align with the individual state and
contribute toward designing systems that align with an individual's state of
wellbeing.
