A Hierarchical Pedestrian Behavior Model to Generate Realistic Human
Behavior in Traffic Simulation
- URL: http://arxiv.org/abs/2206.01601v1
- Date: Wed, 1 Jun 2022 02:04:38 GMT
- Title: A Hierarchical Pedestrian Behavior Model to Generate Realistic Human
Behavior in Traffic Simulation
- Authors: Scott Larter, Rodrigo Queiroz, Sean Sedwards, Atrisha Sarkar,
Krzysztof Czarnecki
- Abstract summary: We present a hierarchical pedestrian behavior model that generates high-level decisions through the use of behavior trees.
A full implementation of our work is integrated into GeoScenario Server, a scenario definition and execution engine.
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.
- Score: 11.525073205608681
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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