@inproceedings{0737bbd99c21469b969f0c61ec49de5b,
title = "Bayesian-networks-based motion estimation for a highly-safe intelligent vehicle",
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.",
keywords = "Bayesian network, Driver's intention, Intelligent vehicle, Learning, Motion estimation",
author = "{Van Dan}, Nguyen and Michitaka Kameyama",
year = "2006",
doi = "10.1109/SICE.2006.315849",
language = "English",
isbn = "8995003855",
series = "2006 SICE-ICASE International Joint Conference",
pages = "6023--6026",
booktitle = "2006 SICE-ICASE International Joint Conference",
note = "2006 SICE-ICASE International Joint Conference ; Conference date: 18-10-2006 Through 21-10-2006",
}