Watch Out! Modelling Pedestrians with Egocentric Distractions

Melissa Kremer, Brandon Haworth, Mubbasir Kapadia, Petros Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The use of mobile devices is one of the most commonly observed family of distracted behaviours exhibited by pedestrians in urban environments. We develop an event-driven behaviour tree model for distracted pedestrians that includes initiating mobile device use as well as terminating or pausing mobile device use based on internal or external cues to refocus attention. We present a simple, probabilistic attention model for such pedestrians. The proposed model is not meant to be complete. It primarily focuses on computing the probability that a distracted agent looks up, based on the agent's individual characteristics and the elements in their environment. We condition the potentially attention grabbing elements in the environment on distraction-specific egocentric fields for visual attention. We also propose an oriented ellipse model for capturing the affects of cognitively fuzzy goals during distracted navigation. Our model is simple and intuitively parameterized, and thus can be easily edited and extended.

Original languageEnglish (US)
Title of host publicationProceedings - MIG 2020
Subtitle of host publication13th ACM SIGGRAPH Conference on Motion, Interaction, and Games
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450381710
DOIs
StatePublished - Oct 16 2020
Event13th ACM SIGGRAPH Conference on Motion, Interaction, and Games, MIG 2020 - Virtual, Online, United States
Duration: Oct 16 2020Oct 18 2020

Publication series

NameProceedings - MIG 2020: 13th ACM SIGGRAPH Conference on Motion, Interaction, and Games

Conference

Conference13th ACM SIGGRAPH Conference on Motion, Interaction, and Games, MIG 2020
Country/TerritoryUnited States
CityVirtual, Online
Period10/16/2010/18/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Human-Computer Interaction
  • Education

Keywords

  • crowd simuation
  • distracted pedestrians
  • visual attention

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