Empirical Prediction of Pedestrian Comfort in Mobile Robot�Pedestrian Encounters
Alireza Jafari, Hong-Son Nguyen, Yen-Chen Liu
AI summary
Problem
Mobile robots in public spaces often cause discomfort because path planners prioritize objective safety over quantifiable subjective comfort.
Approach
Researchers conducted controlled hallway trials with a mobile robot and volunteers, measuring six kinematic variables and correlating them with subjective comfort ratings to develop empirical comfort predictors.
Key results
- Minimum projected time-to-collision (PTTC) shows the strongest correlation with reported comfort.
- Higher robot speeds and smaller minimum distances significantly reduce pedestrian comfort.
- A composite estimator combining all kinematic variables achieves the highest prediction accuracy with an odds ratio of 3.67.
- Lateral distance exhibits a nonmonotonic inverted-U relationship with comfort, peaking around 0.75–1.0 m.
Why it matters
This framework allows path-planning algorithms to quantify and incorporate pedestrian feelings, enabling robots to navigate public spaces more socially acceptably.
Abstract
Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians’ objective safety, for example, by developing col- lision avoidance algorithms, but not enough studies take the pedestrian’s subjective safety or comfort into consideration. Quantifying comfort is a major challenge that hinders mobile robots from understanding and responding to human emo- tions. We empirically look into the relationship between the mobile robot-pedestrian interaction kinematics and subjective comfort. We perform one-on-one experimental trials, each involving a mobile robot and a volunteer. Statistical analysis of pedestrians’ reported comfort versus the kinematic variables shows moderate but significant correlations for most variables. Based on these empirical findings, we design three comfort estimators/predictors derived from the minimum distance, the minimum projected time-to-collision, and a composite estima- tor. The composite estimator employs all studied kinematic variables and reaches the highest prediction rate and classifying performance among the predictors. The composite predictor has an odds ratio of 3.67. In simple terms, when it identifies a pedestrian as comfortable, it is almost 4 times more likely that the pedestrian is comfortable rather than uncomfortable. The study provides a comfort quantifier for incorporating pedestrian feelings into path planners for more socially compliant robots.