Probabilistic Interaction-Aware Occupancy Prediction for Vehicles in Arbitrary Road Scenes

Peter Zechel, Ralph Streiter, Klaus Bogenberger, Ulrich Goehner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper drafts a new interaction-aware approach to predict road user occupancy through vehicle-specific probabilities of presence. Instead of presenting another behavior prediction approach which tries to predict the most likely vehicle positions, the aim is to determine the likelihood of all feasible vehicle positions, called probability of presence. Thus the occupied area derived from this probability of presence blocks less space compared to existing approaches in the field of occupancy prediction. For this purpose, based on a physical model the future presence areas of a vehicle are fully calculated in a discrete consideration. Each movement possibility is evaluated taking into account the statistically typical driver behavior and interactions with static and dynamic objects. This gives rise to a qualified prediction of the future behavior of vehicles which blocks less space compared to all feasible future vehicle positions, like demonstrated in the numerical example.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages423-424
Number of pages2
ISBN (Electronic)9781538692455
DOIs
StatePublished - 26 Mar 2019
Externally publishedYes
Event3rd IEEE International Conference on Robotic Computing, IRC 2019 - Naples, Italy
Duration: 25 Feb 201927 Feb 2019

Publication series

NameProceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019

Conference

Conference3rd IEEE International Conference on Robotic Computing, IRC 2019
Country/TerritoryItaly
CityNaples
Period25/02/1927/02/19

Keywords

  • advanced computing applications
  • autonomous vehicles
  • behavior prediction
  • occupancy prediction

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