Figure 1: Animations resulting from our simulations. Emergent self-organized patterns appear in real crowds of walkers. Our simulations display similar effects by proposing an optic flow-based approach for steering walkers inspired by cognitive science works on the human locomotion. Compared to previous approaches, our model improves such an emergence as well as the global efficiency of walkers traffic. We thus enhance the overall believability of animations by avoiding improbable locking situations.

This paper addresses the problem of virtual pedestrian autonomous navigation for crowd simulation. It describes a method for solving interactions between pedestrians and avoiding inter-collisions. Our approach is agent-based and predictive: each agent perceives surrounding agents and extrapolates their trajectory in order to react to potential collisions. We aim at obtaining realistic results, thus the proposed model is calibrated from experimental motion capture data. Our method is shown to be valid and solves major drawbacks compared to previous approaches such as oscillations due to a lack of anticipation. We first describe the mathematical representation used in our model, we then detail its implementation, and finally, its calibration and validation from real data.

International audienceAn interaction occurs between two humans when they walk with converging trajectories. They need to adapt their motion in order to avoid and cross one another at respectful distance. This paper presents a model for solving interactions between virtual humans. The proposed model is elaborated from experimental interactions data. We first focus our study on the pair-interaction case. In a second stage, we extend our approach to the multiple interactions case. Our experimental data allow us to state the conditions for interactions to occur between walkers, as well as each one's role during interaction and the strategies walkers set to adapt their motion. The low number of parameters of the proposed model enables its automatic calibration from available experimental data. We validate our approach by comparing simulated trajectories with real ones. We also provide comparison with previous solutions. We finally discuss the ability of our model to be extended to complex situations

The navigation activity is an every day practice for any human being capable of locomotion.