Mixture-based cluster detection in driving-related data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F15%3A00231582" target="_blank" >RIV/68407700:21260/15:00231582 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/SCSP.2015.7181548" target="_blank" >http://dx.doi.org/10.1109/SCSP.2015.7181548</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SCSP.2015.7181548" target="_blank" >10.1109/SCSP.2015.7181548</a>

Result language

angličtina

Original language name

Mixture-based cluster detection in driving-related data

Original language description

The paper deals with detection of clusters in data measured on a driven vehicle. Such a clustering aims at distinguishing various driving styles for eco-driving and driver assistance systems. The task is solved with the help of the application of the recursive Bayesian mixture estimation theory. The main contribution of the paper is a demonstration that real measurements with non-linear relations.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

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Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2015 Smart Cities Symposium Prague (SCSP)

ISBN

978-1-4673-6727-1

ISSN

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e-ISSN

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Number of pages

7

Pages from-to

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Publisher name

IEEE Press

Place of publication

New York

Event location

Prague

Event date

Jun 24, 2015

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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