The quality of a crowd simulation model is determined by its agents' local and global trajectory efficiency. While an agent-based model can accurately handle the local trajectories, global decisions usually are handled by a global path planner. However, most of the global path planning techniques do not consider other agents and their possible paths and the future global flow in the environment. In this paper, we propose a composite system that takes future agent configurations into account via a modified A* algorithm to create a global path plan and combines the global path plan with a local navigation model. We show that the agents using the proposed model intelligently plan their paths based on the dynamic configuration of the environment. In order to balance the performance vs. trajectory quality trade-off, we propose a hierarchical grid structure and discuss its effects on both trajectory quality and computational performance.

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In this study, a hardware‐accelerated dynamic clustering of moving virtual entities technique is proposed. By clustering virtual entities, both clustered and unclustered virtual agents became more aware of other agents’ topological configurations. Clustering is based on their continuously changing velocity and position vectors. The proposed clustering technique efficiently uses graphics processor's parallel processing capabilities. Therefore, almost no additional central processing unit overhead is required to bring the clustering information into the simulation. In addition, in this paper, how cluster information can be used on top of the proposed virtual human steering technique is explained. The results show that by using the dynamic clustering, the number of collision in the simulation reduces, and the velocities of the the agents in the simulation increase. Copyright © 2012 John Wiley & Sons, Ltd.

In a competent crowd navigation system, it is very important for the agents in the system to plan their movements being aware of the other agents. In this study, we propose the use of machine learning methods to create time‐based global path plans by utilizing the information as to when and where the other agents would be at a future time. The application of a machine learning method in the traditional manner for the global path planning problem is not a straightforward task due to the complexity of data collection; therefore, this study proposes a novel method to apply machine learning methods for global path planning. This enables us to create a context‐free model. We organize experiments to compare our method to a recent and competitive approach that is referred to as the potential‐based method (PBM). We employed three different machine learning methods, namely, artificial neural networks, polynomial regression, and support vector regression. The results of the mass scenario tests and a corridor scenario indicate that the versions with polynomial regression and support vector regression outperform the PBM. This encourages further investigations on the use of machine learning methods for global path planning in crowd navigation.