Risky and crowded environments (RCE) contain abstract sources of risk and uncertainty, which are perceived differently by humans, leading to a variety of behaviors. Thus, robots deployed in RCEs, need to exhibit diverse perception and planning capabilities in order to interpret other human agents' behavior and act accordingly in such environments. To understand this problem domain, we conducted a study to explore human path choices in RCEs, enabling better robotic navigational explainable AI (XAI) designs. We created a novel COVID-19 pandemic grocery shopping scenario which had time-risk tradeoffs, and acquired users' path preferences. We found that participants showcase a variety of path preferences: from risky and urgent to safe and relaxed. To model users' decision making, we evaluated three popular risk models (Cumulative Prospect Theory (CPT), Conditional Value at Risk (CVAR), and Expected Risk (ER). We found that CPT captured people's decision making more accurately than CVaR and ER, corroborating theoretical results that CPT is more expressive and inclusive than CVaR and ER. We also found that people's self assessments of risk and time-urgency do not correlate with their path preferences in RCEs. Finally, we conducted thematic analysis of open-ended questions, providing crucial design insights for robots is RCE. Thus, through this study, we provide novel and critical insights about human behavior and perception to help design better navigational explainable AI (XAI) in RCEs.

翻译：风险与拥挤环境（RCE）包含抽象的风险源与不确定性，人类对其感知存在差异，从而产生多样化行为。因此，部署在RCE中的机器人需要具备多元化的感知与规划能力，以理解其他智能体的行为并做出相应响应。为深入理解这一领域，我们开展了一项关于RCE中人类路径选择的研究，旨在优化机器人导航的可解释人工智能（XAI）设计。我们创设了一个具有时间-风险权衡的新型COVID-19疫情超市购物场景，并通过实验获取用户的路径偏好。研究发现，参与者表现出从高风险紧迫型到安全舒缓型的多样化路径偏好。为建模用户的决策过程，我们评估了三种主流风险模型：累积前景理论（CPT）、条件风险价值（CVaR）与期望风险（ER）。结果表明，CPT比CVaR和ER能更准确地预测人类决策，验证了CPT在表达力与包容性上优于后两者的理论结论。此外，我们发现用户对风险与时间紧迫性的自我评估与其在RCE中的路径偏好无显著相关性。最后，通过对开放式问题的主题分析，我们提炼出RCE中机器人导航的关键设计洞见。本研究为设计RCE中更具解释性的导航XAI提供了关于人类行为与感知的新颖且重要的见解。