Pattern Based Motion for Crowd Simulation

We present a pattern-based approach for simulating the steering behavior of pedestrians, which aims to imitate the way that real pedestrians perceive spatial-temporal information and make steering decisions in daily-life situations. Novel representations of spatial-temporal patterns are proposed that allow modelers to intuitively and naturally specify some prototypical patterns for various steering behaviors. Based on the spatial-temporal patterns, a hierarchical pattern matching process has been developed, which simulates how pedestrians process spatial temporal information and make steering decisions. Experimental results show that this new approach is quite promising and capable of producing human-like steering. We hope that the idea presented in this paper can direct researchers in this area with a fresh perspective.