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This paper investigates the relationship between robot-pedestrian interaction kinematics and pedestrian comfort through controlled experiments with human subjects. Statistical analysis reveals significant correlations between kinematic variables (minimum distance, projected time-to-collision) and reported comfort levels. The authors then develop and compare three comfort estimators, finding that a composite estimator using all kinematic variables achieves the highest prediction accuracy, with an odds ratio of 3.67.
Turns out, you *can* predict pedestrian comfort levels from robot kinematics, paving the way for socially aware robots that don't just avoid collisions, but also avoid making people feel uneasy.
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.