Empirical Prediction of Pedestrian Comfort in Mobile Robot Pedestrian Encounters

2026-04-15

AI Summary

This study addresses the challenge of socially compliant navigation in mobile robotics by explicitly modeling pedestrians’ subjective comfort, which existing approaches often overlook. Through one-on-one human–robot interaction experiments, the authors simultaneously collect kinematic data and subjective comfort ratings to establish statistical relationships between comfort and key motion variables—such as minimum distance and time-to-collision. They propose the first quantitative predictive model of pedestrian comfort that integrates multiple motion-related features. A logistic regression–based classifier significantly enhances the system’s ability to recognize subjective comfort states, achieving an odds ratio of 3.67; that is, when the model predicts “comfortable,” pedestrians are nearly four times more likely to actually feel comfortable than uncomfortable. This model provides an emotion-aware module readily embeddable into path-planning frameworks for socially intelligent navigation.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• pedestrian comfort
• mobile robot
• subjective safety
• human-robot interaction
• comfort quantification

Innovation

• pedestrian comfort
• mobile robot navigation
• human-robot interaction
• comfort prediction
• social compliance