Bayesian-networks-based motion estimation for a highly-safe intelligent vehicle

Nguyen Van Dan, Michitaka Kameyama

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

8 Citations (Scopus)

Abstract

Motion estimation of a moving object is one of the most important technologies to develop a next-generation highly-safe intelligent vehicle. Although intention of a driver in a target vehicle is key information for the motion estimation, we can not observe directly from sensors. This article presents a building method of Bayesian Networks (BNs) for motion estimation related to a driver's intention. Driver's intentions are hierarchically defined, so that the BN becomes as simple as possible. Causal relation between the intentions is discussed to reflect the real-world motion process. As a result, not only the quality of motion estimation but also the inference performance can be increased, Experimental learning system based on two-dimensional image processing is also presented for automatic acquisition of the BN probabilistic parameters.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Pages6023-6026
Number of pages4
DOIs
Publication statusPublished - 2006 Dec 1
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference

Other

Other2006 SICE-ICASE International Joint Conference
CountryKorea, Republic of
CityBusan
Period06/10/1806/10/21

Keywords

  • Bayesian network
  • Driver's intention
  • Intelligent vehicle
  • Learning
  • Motion estimation

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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