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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.
Yuanyuan Peng andMasaki 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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Yuanyuan Peng, 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