## Abstract

Pedestrians do not walk randomly. While they move toward their desired destination, they avoid static obstacles and other pedestrians. At the same time they try not to slow down too much as well as not to speed up excessively. Studies coming from the field of social psychology show that pedestrians exhibit common behavioral patterns. For example the distance at which one individual keeps himself from others is not uniformly random, but depends on the acquaintance level of the individuals, the culture and other factors. Our goal here is to use this knowledge to build a model that probabilistically represents the future state of a pedestrian trajectory. To this end, we focus on a stochastic motion model that caters for the possible behaviors in an entire scene in a multi-hypothesis approach, using a principled modeling of uncertainties.

## Keywords

Gaussian Mixture Model Motion Model Gibbs Measure World Model Static Obstacle## References

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