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.

翻译：近期自动驾驶汽车（AV）技术与微出行设备的进展预示着未来出行方式的变革性转变。这些进展也引发了人机交互方面的挑战。为确保这些新型出行方式的顺利普及，必须评估人们过去的社会互动经验与感知如何影响其与AV的交互。本研究基于个体的行为、先前经验、社会互动感知以及与其他道路使用者的二元互动，识别并估算其福祉。我们设计了一项基于在线视频的用户研究，收集并分析了300名参与者的反馈，以探讨对个体福祉的影响。通过构建机器学习模型预测福祉变化，并基于该模型制定最优政策，指导AV在与其他道路使用者让行行为中的决策，从而提升用户福祉。本研究的结果对构建以人为本的系统具有广泛意义，通过制定与个体状态相匹配的政策，为设计贴合个体福祉状态系统提供支撑。