  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.
