Finding Meaningful Robust Chunks from Driving Behavior Based on Double Articulation Analyzer

Finding Meaningful Robust Chunks from Driving Behavior Based on Double Articulation Analyzer

The double articulation analyzer is a machine learning algorithm which can extract double articulation structure from time series data based on nonparametric Bayesian approach. The method was proved to detect intentional changes of a driver from time series data recorded by an instrumented vehicle....

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Veröffentlicht in:Keisoku Jidō Seigyo Gakkai ronbunshū 2013, Vol.49(11), pp.1047-1056
Hauptverfasser: TANIGUCHI, Tadahiro, YAMASHITA, Genki, NAGASAKA, Shogo, BANDO, Takashi, TAKENAKA, Kazuhito, HITOMI, Kentarou
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Sprache:eng ; jpn
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double articulation
driving behavior
nested Pitman-Yor language model
sticky hierarchical Dirichlet process-hidden Markov model
time-series modelling
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container_end_page 1056
container_issue 11
container_start_page 1047
container_title Keisoku Jidō Seigyo Gakkai ronbunshū
container_volume 49
creator TANIGUCHI, Tadahiro
YAMASHITA, Genki
NAGASAKA, Shogo
BANDO, Takashi
TAKENAKA, Kazuhito
HITOMI, Kentarou
description The double articulation analyzer is a machine learning algorithm which can extract double articulation structure from time series data based on nonparametric Bayesian approach. The method was proved to detect intentional changes of a driver from time series data recorded by an instrumented vehicle. In this paper, we segment time series data obtained during a driver drove a car through two types of courses using the double articulation analyzer, and analyze the extracted robust chunks by comparing with tags which were added to the recorded data by human participants.
doi_str_mv 10.9746/sicetr.49.1047
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subjects double articulation
driving behavior
nested Pitman-Yor language model
sticky hierarchical Dirichlet process-hidden Markov model
time-series modelling
title Finding Meaningful Robust Chunks from Driving Behavior Based on Double Articulation Analyzer
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