A collision avoidance behavior model for crowd simulation based on psychological findings

This paper proposes a collision avoidance behavior model for crowd simulation based on psychological findings of human behaviors such as gaze-movement angle (GMA), side stepping, gait motion, and personal reaction bubble (PRB) to get better results in crowd simulation. By calculating the GMA between agents, collision can be predicted and avoided without knowing the exact trajectories of the agents. The proposed model consists of four phases: (1) GMA-based collision prediction for mid/long range by using speed-variant Information Process Space (IPS), (2) collision avoidance steering, (3) gait-based locomotion generation, and (4) space-keeping based on PRB. The effectiveness of the proposed speed-variant IPS was tested on various types of agent flows with different densities. The total loss of kinetic energy accumulated during an agent’s movement and the ratio of the length of the path actually traveled to the length of the original path are used as key metrics to figure out the features between the different types of flows. Finally, examples of tuning the parameters with well-known fundamental diagrams are presented.