A probabilistic model of pedestrian crossing behavior at signalized intersections for connected vehicles

Yoriyoshi Hashimoto, Yanlei Gu, Li Ta Hsu, Miho Iryo-Asano, Shunsuke Kamijo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

32 Citations (Scopus)

Abstract

Active safety systems which assess highly dynamic traffic situations including pedestrians are required with growing demands in autonomous driving and Connected Vehicles. In this paper, we focus on one of the most hazardous traffic situations: the possible collision between a pedestrian and a turning vehicle at signalized intersections. This paper presents a probabilistic model of pedestrian behavior to signalized crosswalks. In order to model the behavior of pedestrian, we take not only pedestrian physical states but also contextual information into account. We propose a model based on the Dynamic Bayesian Network which integrates relationships among the intersection context information and the pedestrian behavior in the same way as a human. The particle filter is used to estimate the pedestrian states, including position, crossing decision and motion type. Experimental evaluation using real traffic data shows that this model is able to recognize the pedestrian crossing decision in a few seconds from the traffic signal and pedestrian position information. This information is assumed to be obtained with the development of Connected Vehicle.

Original languageEnglish
Pages (from-to)164-181
Number of pages18
JournalTransportation Research Part C: Emerging Technologies
Volume71
DOIs
Publication statusPublished - 1 Oct 2016
Externally publishedYes

Keywords

  • Active safety system
  • Connected vehicle
  • Dynamic Bayesian Network
  • Pedestrian behavior
  • Signalized intersection

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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