PopNavShift：行为群体偏移下的社交导航压力测试
PopNavShift: Stress-Testing Social Navigation under Behavioral Population Shift
- New York University（纽约大学）
- NYU Shanghai（上海纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
PopNavShift提出匹配仿真框架，通过群体条件化行人轮廓压力测试社交导航策略，发现时间压力变化显著影响行人负担相关排名，强调跨群体评估的必要性。
AI中文摘要：
社交导航算法通常在固定的行人行为分布下进行评估，尽管不同个体和社交情境中行人对机器人的反应存在显著差异。我们提出了PopNavShift，一个匹配的仿真框架，用于在行人群体偏移下对社交导航策略进行压力测试。PopNavShift通过使用MatrAIx Persona 1M中的600条合成人物档案提示Gemini 3.7 Flash，并将响应确定性映射为有界运动参数，从而构建群体条件化的行人运动轮廓。随后，它在八种群体条件和7,488次匹配的机器人运行中比较了三种代表性导航策略：反应式避让、早期让行和互惠碰撞避免。在对相同202个人物进行的匹配干预中，仅改变时间压力就使基于机器人行驶时间的控制器排名反转了8.6%，而基于平均行人延误的排名反转了22.4%，基于最差十分位延误的排名反转了23.9%。在各种群体条件下，这种敏感性对行人负担的影响大于对机器人行驶时间的影响，并且在空间受限的环境中有所增加；在第二种行人动力学模型下，相同的定性模式仍然存在。这些发现支持在行为群体间使用机器人性能和行人负担两个指标来评估导航策略。
英文摘要：
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.
