Empirical Prediction of Pedestrian Comfort in Mobile Robot Pedestrian Encounters
Provides a quantifiable comfort metric for mobile robots to incorporate pedestrian feelings into path planning, addressing the underexplored area of subjective safety in human-robot interaction.
This study empirically investigates how mobile robot-pedestrian interaction kinematics affect subjective comfort, finding moderate correlations. A composite estimator using multiple kinematic variables achieves the best prediction performance with an odds ratio of 3.67, indicating that when it predicts comfort, the pedestrian is nearly 4 times more likely to be comfortable.
Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians' objective safety, for example, by developing collision 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 emotions. 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 estimator. 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.