Context-based pedestrian path prediction

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

180 Citations (Scopus)


We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
Number of pages16
Volume8694 LNCS
EditionPART 6
ISBN (Print)9783319105987
Publication statusPublished - 2014
Externally publishedYes
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: 6 Sep 201412 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 6
Volume8694 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference13th European Conference on Computer Vision, ECCV 2014


  • Dynamic Bayesian Network
  • intelligent vehicles
  • Linear Dynamical System
  • path prediction
  • situational awareness
  • visual focus of attention


Dive into the research topics of 'Context-based pedestrian path prediction'. Together they form a unique fingerprint.

Cite this