Wellbeing in Future Mobility: Toward AV Policy Design to Increase Wellbeing through Interactions

TL;DR

This study explores how automated vehicle interactions influence individual wellbeing, using a machine learning model to predict wellbeing changes and inform AV policies that promote positive social interactions.

Contribution

It introduces a novel approach to assess and predict individual wellbeing based on social interactions and prior experiences, guiding AV policy design for enhanced wellbeing.

Findings

Machine learning model accurately predicts wellbeing changes.

Optimal AV yielding policies can enhance user wellbeing.

Insights into social interaction perceptions affecting wellbeing.

Abstract

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.