  Modelling pedestrian behavior is crucial in the development and testing of
autonomous vehicles. In this work, we present a hierarchical pedestrian
behavior model that generates high-level decisions through the use of behavior
trees, in order to produce maneuvers executed by a low-level motion planner
using an adapted Social Force model. A full implementation of our work is
integrated into GeoScenario Server, a scenario definition and execution engine,
extending its vehicle simulation capabilities with pedestrian simulation. The
extended environment allows simulating test scenarios involving both vehicles
and pedestrians to assist in the scenario-based testing process of autonomous
vehicles. The presented hierarchical model is evaluated on two real-world data
sets collected at separate locations with different road structures. Our model
is shown to replicate the real-world pedestrians' trajectories with a high
degree of fidelity and a decision-making accuracy of 98% or better, given only
high-level routing information for each pedestrian.
