Controlling agents using recurrent neural network in crowd simulation

Yuanyuan Peng; Masaki Oshita

doi:10.1117/12.2591025

13 March 2021 Controlling agents using recurrent neural network in crowd simulation

Yuanyuan Peng, Masaki Oshita

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117660S (2021) https://doi.org/10.1117/12.2591025
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

We use deep learning technique for predicting temporary target position of each controlling agent in crowd simulation. Our deep learning model is based on recurrent neural network, which can predict a heatmap that represents the surrounding situation of an agent from a series of previous heatmaps of the agent. A heatmap contains positions and speeds of nearby agents and the temporary target position which represents the heading direction of the agent. Each agent is controlled based on the temporary target position that is chosen from an estimated heatmap using a force-based model.

Citation Download Citation

Yuanyuan Peng and Masaki Oshita "Controlling agents using recurrent neural network in crowd simulation", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117660S (13 March 2021); https://doi.org/10.1117/12.2591025

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