用新损失函数降低行人碰撞率，提升仿真真实度
Simulation of collision avoidance behavior in crowd movement by data-driven approach
- 将碰撞机制融入损失函数，通过侧向加速度建模
- 双向流中碰撞率显著下降，接近实验水平
- 适合交通规划与安全评估场景使用
人群移动模拟对行人安全管理与设施布局优化至关重要。数据驱动模型在欧氏度量下提升了轨迹预测精度，但在双向和多向流动中仍存在过高碰撞率问题。本文提出一种新型数据驱动人群模拟模型，将行人碰撞机制引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。模型基于生成对抗网络（GAN）架构，命名为CPGAN（Collision-Penalized GAN）。在双向流场景下评估，该场景频繁发生避让行为。结果表明，所提出的侧向加速度碰撞损失显著降低了对向行人碰撞率，达到与受控实验相当的水平。CPGAN有效模拟了双向流，再现了车道形成和N-t曲线。研究结果为将行人动力学机制融入数据驱动模拟的损失函数提供了新思路。
原文摘要 · Abstract (English)
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.
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