@proceedings{4,
  keywords = {Adaptation models, Behavioral sciences, Data models, Roads, Social force, Traffic control, Trajectory},
  author = {Scott Larter and Rodrigo Queiroz and Sean Sedwards and Atrisha Sarkar and Krzysztof Czarnecki},
  title = {A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation},
  abstract = {<p>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.</p>
},
  year = {2022},
  pages = {533\textendash541},
  month = {07/2022},
  publisher = {IEEE},
  address = {Aachen, Germany},
  doi = {10.1109/IV51971.2022.9827035},
}