As autonomous mobile robots (AMRs) are increasingly introduced into diverse environments, it is crucial that they interact smoothly and naturally with nearby pedestrians. To allow this, AMRs are expected to communicate and behave in a manner similar to interactions between humans. This study addresses pedestrian-pedestrian passing behavior and presents a novel quantitative index, termed the inducibility measure, that captures the extent to which a person’s actions can affect the behavior of those around them. Two types of inducibility measures are proposed: one derived from the sensitivity of the decision-making process, and the other from the controllability Gramian of a state-space representation of a closed-loop system representing interactive behavior. These measures were analyzed using a mathematical model of pedestrian behavior developed from actual observational data on pedestrian interactions. The proposed indices are intended to support the design and evaluation of AMR behavior, particularly in scenarios involving close interactions with humans.

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