Concurrent Hierarchical Finite State Machines for Modeling Pedestrian Behavioral Tendencies

Abstract A detailed and realistic simulation of pedestrian behavior requires a precise modeling of human decision making processes regarding action prioritization and selection. This paper presents a novel strategic pedestrian decision making model based on concurrent hierarchical finite state machines. The model is capable to create goal directed behavioral tendencies, which are operational guardrails for tactical models. The decision model is inherently flexible and scalable, being adaptable to different application scenarios, behavioral demands and input parameters. The applicability of the model is shown by means of an example. Furthermore, we discuss a validation and calibration approach for the decision model.

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