Empirical Pedestrian Safety Assessment in a Mobile Robot Using a Predictive Social Force Model

2026-07-10

AI Summary

This study addresses the challenge of ensuring both objective safety and subjective comfort for mobile robots operating alongside pedestrians on sidewalks. For the first time, it systematically evaluates the integrated safety performance of the classical Social Force Model (SFM), its time-extended variant (TSFM), and two predictive extensions—the Predictive Social Force Model (PSFM) and a PTTC-based PTSFM—on a real non-holonomic robotic platform in single-pedestrian interaction scenarios. Experimental results, combining objective safety metrics with subjective Likert-scale assessments, demonstrate that incorporating projected time-to-collision (PTTC) significantly enhances objective safety. In contrast, the finite-horizon prediction mechanism yields only marginal improvements on select sub-metrics and produces no statistically significant differences in subjective user comfort.

Abstract

Mobile robots are going to share the sidewalks with pedestrians. They must ensure their objective safety and respect the walkers' subjective safety/comfort. Computationally efficient Social Force Models (SFM) present interpretable solutions for real-time robot navigation in dynamic crowds. Recent explorations of Projected Time-to-collision (PTTC) integration into SFM variants, for example, PTTC-based SFM (TSFM), improve safety metrics. But the effect of predictive variants is unclear. We introduce Predictive SFM (PSFM) and Predictive TSFM (PTSFM) by integrating predicted social force vectors over a finite time horizon. The paper implements SFM, TSFM, PSFM, and PTSFM on a nonholonomic mobile robot and performs experimental trials with volunteers attending a facing scenario. We systematically study objective and subjective safety across the variants. Minimum PTTC, average speed, minimum distance, lateral distance, and the maximum trajectory curvature benchmark the objective safety. Likert scale post-interaction surveys assess subjective safety by marking comfort, smoothness, distance appropriateness, and speed suitability. We confirm that PTTC integration improves safety metrics. The prediction contribution is limited and occasionally visible in some of the sub-metrics. Some participants perceive smoother movements and safer speed behavior with predictive methods, but Mann-Whitney tests reveal no significant differences in subjective ratings. Therefore, PTTC-based navigation enhances safety, whereas the formulated prediction offers limited additional benefits in single-pedestrian scenarios.

Problem

• pedestrian safety
• mobile robot
• social force model
• subjective comfort
• objective safety

Innovation

• Predictive Social Force Model
• Projected Time-to-collision
• mobile robot navigation
• subjective safety
• human-robot interaction