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arXiv · 2206.01601

A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation

Scott Larter·Rodrigo Queiroz·Sean Sedwards·Atrisha Sarkar·Krzysztof Czarnecki
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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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Scott Larter, Rodrigo Queiroz, Sean Sedwards, Atrisha Sarkar, Krzysztof Czarnecki. 2022-06-01. A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation. https://doi.org/10.1109/iv51971.2022.9827035

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