A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation
This work addresses the need for accurate pedestrian simulation in autonomous vehicle testing, though it is incremental as it builds on existing models like Social Force.
The authors tackled the problem of generating realistic pedestrian behavior for autonomous vehicle testing by developing a hierarchical model that combines behavior trees with an adapted Social Force model, achieving over 98% decision-making accuracy in replicating real-world trajectories.
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.