Driver's intention estimation based on Bayesian Networks for a highly-safe intelligent vehicle

Bo Sun, Michitaka Kameyama

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Highly safe intelligent vehicles can significantly reduce vehicle accidents by warning drivers of dangerous situations. Trajectory estimation of target vehicles is expected to be used in highly safe intelligent vehicles. Trajectory estimation requires that we estimate driver intent not detectable by sensors. The Bayesian Network (BN) building we propose for trajectory estimation related to driver intent defines driver intent hierarchically to simplify the BN as much as possible. Causal driver-intent relationships are discussed reflecting real-world motion. This raises the quality of driver-intent estimation and increasing inference performance. Experimental learning based on 2D image processing is presented to acquire probabilistic BN parameters.

本文言語English
ページ(範囲)219-225
ページ数7
ジャーナルJournal of Robotics and Mechatronics
24
1
DOI
出版ステータスPublished - 2012 2

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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