@misc{5,
  keywords = {Autonomous vehicles, Behaviour model, Behaviour trees, Pedestrian simulation, Scenario based testing, Social force model},
  author = {Scott Larter},
  title = {A Hierarchical Pedestrian Behaviour Model to Reproduce Realistic Human Behaviour in a Traffic Environment},
  abstract = {<p>Understanding pedestrian behaviour in traffic environments is a crucial step in the development and testing of autonomous vehicles. As the environment\&amp;$\#$39;s most vulnerable road users, pedestrians introduce an element of unpredictability that can lead to dangerous scenarios if their behaviours are unfamiliar to or misinterpreted by vehicles. In this thesis, we present a hierarchical pedestrian behaviour model that interprets high-level decisions through the use of behaviour trees to produce maneuvers that are executed by the low-level motion planner using an adapted Social Force Model. The presented hierarchical model is evaluated on two real-world data sets collected at separate locations with different road structures. The first data set provides a busy four-way intersection with signalized crosswalks, while the second location provides an unsignalized crosswalk across a two-way road at a Canadian university.</p>
},
  year = {2022},
  volume = {Master of Mathematics},
  month = {03/2022},
  url = {http://hdl.handle.net/10012/18094},
}