@inproceedings{1489615f8e8747bfb71368d17bbc9ed0,
title = "Deep Learning Improved Social Force Model to Simulate Crowd in Varying Scenes",
abstract = "Traditional social force models are capable of simulating various types of crowds, but due to their reliance on a single formula and simplified parameter control, they often lack realism when applied to crowd scenes. The integration of deep learning with social force models has already achieved success in pedestrian simulation tasks in specific scenarios, yet research in more generalized scenes is still lacking. Therefore, we have used deep learning in conjunction with social force models to construct crowd simulations in various scenarios. This model is based on the dynamics of crowd trajectories and refines key parameters of the social force model using deep learning methods. Experimental results prove that this model can effectively adapt to diverse environments different from the scenes on which the training data is based.",
keywords = "Crowd Simulation, Deep Learning, Multi-Agent, Social Force Model",
author = "Dapeng Yan and Chongzhi Bai and Lian He and Gangyi Ding",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024 ; Conference date: 01-03-2024 Through 03-03-2024",
year = "2024",
doi = "10.1109/ICAACE61206.2024.10548157",
language = "English",
series = "2024 7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "307--312",
booktitle = "2024 7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024",
address = "United States",
}