Several recent works consider the personalized route planning based on user profiles, none of which accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This paper presents the first trust-based route planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing human's hidden mental state. We designed and conducted an online user study with 100 participants on the Amazon Mechanical Turk platform to collect data of users' trust in automated vehicles. We build data-driven models of trust dynamics and takeover decisions, which are incorporated in the POMDP framework. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning. We evaluated the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally resulted in higher cumulative POMDP rewards and reported more positive responses in the after-driving survey than those taking the baseline trust-free route.
翻译:最近的几项工作考虑了基于用户概况的个人化路线规划,其中没有任何一项是人类信任的。我们认为,在规划自动车辆路线时,人类信任是一个重要的考虑因素。本文件介绍了第一种基于信任的自动车辆路线规划方法。我们正式将载人车辆互动作为部分可见的Markov决策程序(POMDP)和模型信任作为部分可观察的POMDP变量,代表人类隐藏的精神状态。我们设计并在亚马逊机械土耳其平台上与100名参与者进行了在线用户研究,以收集用户对自动车辆信任的数据。我们建立了数据驱动的信任动态和接管决定模型,这些模型已纳入POMDP框架。我们通过在POMDP规划中找到最佳政策,为自动车辆设计了最佳路线。我们与22名参与者一起通过驾驶模拟器对由此形成的路径进行了人体实验。实验结果表明,采用基于信任的路线的参与者一般会获得更高的累积的POMDP奖赏,并在后驱动调查中报告比使用基线无信任路线的人更积极的反应。