基于数据驱动方法的群体运动碰撞规避行为模拟
Simulation of collision avoidance behavior in crowd movement by data-driven approach
AI总结：
针对数据驱动人群模拟中碰撞率高的问题，提出一种结合碰撞惩罚的生成对抗网络（CPGAN），通过侧向加速度碰撞损失函数和Voronoi特征提取方法，有效降低双向流中的对向碰撞率。
摘要：
人群运动模拟对于行人安全管理和设施布局优化至关重要。数据驱动模型提高了欧几里得度量下的轨迹预测精度，但存在碰撞率过高的问题，尤其是在双向和多向流中。本文建立了一种新颖的数据驱动人群模拟模型，将行人碰撞机制纳入损失函数以减少碰撞。提出了基于侧向加速度的碰撞损失函数和基于Voronoi的运动特征提取方法。该模型基于生成对抗网络（GAN）架构，称为CPGAN（碰撞惩罚GAN）。我们在涉及频繁碰撞规避行为的双向流场景中评估了CPGAN。结果表明，所提出的基于侧向加速度的碰撞损失显著降低了相反方向行人的碰撞率，达到与受控实验相当的水平。CPGAN有效模拟了双向流，再现了通道形成和N-t曲线。研究成果可为将行人动力学机制融入数据驱动人群模拟的损失函数提供启发。
英文摘要：
Crowd movement simulation is essential for pedestrian safety management and facility layout optimization. Data-driven models enhance trajectory prediction accuracy under Euclidean metrics, yet they suffer from excessively high collision rates, especially in bidirectional and multidirectional flows. In this paper, we establish a novel data-driven crowd simulation model that incorporates the pedestrian collision mechanism into the loss function to reduce collisions. A new lateral-acceleration-based collision loss function and a Voronoi-based motion feature extraction approach are proposed. The model is based on a Generative Adversarial Network (GAN) architecture and is termed CPGAN (Collision-Penalized GAN). We evaluate CPGAN in bidirectional flow scenarios, which involve frequent collision avoidance behaviors. Results show that the proposed lateral-acceleration-based collision loss significantly reduces opposite-direction pedestrian collision rates to levels comparable with controlled experiments. CPGAN effectively simulates bidirectional flow, reproducing lane formation and N-t curves. The research outcomes can provide inspiration for integrating pedestrian dynamics mechanisms into loss functions in data-driven crowd simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
