PopNavShift: Stress-Testing Social Navigation under Behavioral Population Shift

2026-09-18

AI Summary

研究通过PopNavShift框架测试社会导航算法在行人行为变化下的表现，使用Gemini 3.7 Flash生成行人运动模型，并对比了三种导航策略的效果。

Abstract

Social-navigation algorithms are often evaluated under a fixed pedestrian-behavior distribution, despite substantial variation in pedestrian responses to robots across individuals and social contexts. We introduce PopNavShift, a matched simulation framework for stress-testing social-navigation strategies under pedestrian population shifts. PopNavShift constructs population-conditioned pedestrian motion profiles by prompting Gemini 3.7 Flash with 600 synthetic persona records from MatrAIx Persona 1M and deterministically mapping the responses into bounded motion parameters. It then compares three representative navigation strategies, reactive avoidance, early yielding, and reciprocal collision avoidance, across eight population conditions and 7,488 matched robot runs. In a matched intervention on the same 202 personas, changing only time pressure reverses 8.6% of controller rankings based on robot travel time, but 22.4% based on mean pedestrian delay and 23.9% based on worst-decile delay. Across population conditions, this sensitivity is greater for pedestrian burden than for robot travel time and increases in spatially constrained settings; the same qualitative pattern persists under a second pedestrian dynamics model. These findings support evaluating navigation strategies across behavioral populations using both robot performance and pedestrian burden.

Problem

• social navigation
• pedestrian behavior
• population shift
• stress testing

Innovation

• PopNavShift
• social navigation
• behavioral population shift
• pedestrian dynamics
• navigation strategy evaluation