提出轻量级人群预测模型，让机器人高效安全地在人群中导航。
A Lightweight Crowd Model for Robot Social Navigation
- 基于行人流特性简化时空处理，降低计算开销。
- 推理速度提升3.6倍，预测准确率提高3.1%。
- 适合部署在资源受限的机器人系统中，提升社交导航能力。
在人类密集环境中运行的机器人需安全高效地导航，同时最小化社会干扰。这要求实时估算人群运动以避开拥堵区域。传统微观模型在人群密集时因计算成本过高难以扩展，而现有宏观预测模型要么过于简化，要么计算复杂。本文提出一种面向人类运动的轻量级、实时宏观人群预测模型，在预测精度与计算效率间取得平衡。该方法基于行人流的固有特性，简化空间与时间处理，无需复杂架构即可实现鲁棒泛化。实验显示，推理时间减少3.6倍，预测准确率提升3.1%。集成至社交感知规划框架后，模型使机器人在动态环境中实现高效且符合社交规范的导航。研究表明，高效的群体建模可使机器人在不依赖高成本计算的前提下，安全穿越密集环境。
原文摘要 · Abstract (English)
Robots operating in human-populated environments must navigate safely and efficiently while minimizing social disruption. Achieving this requires estimating crowd movement to avoid congested areas in real-time. Traditional microscopic models struggle to scale in dense crowds due to high computational cost, while existing macroscopic crowd prediction models tend to be either overly simplistic or computationally intensive. In this work, we propose a lightweight, real-time macroscopic crowd prediction model tailored for human motion, which balances prediction accuracy and computational efficiency. Our approach simplifies both spatial and temporal processing based on the inherent characteristics of pedestrian flow, enabling robust generalization without the overhead of complex architectures. We demonstrate a 3.6 times reduction in inference time, while improving prediction accuracy by 3.1 %. Integrated into a socially aware planning framework, the model enables efficient and socially compliant robot navigation in dynamic environments. This work highlights that efficient human crowd modeling enables robots to navigate dense environments without costly computations.
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