An online estimation of driving style using data-dependent pointer model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00481220" target="_blank" >RIV/67985556:_____/18:00481220 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.trc.2017.11.001" target="_blank" >http://dx.doi.org/10.1016/j.trc.2017.11.001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.trc.2017.11.001" target="_blank" >10.1016/j.trc.2017.11.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An online estimation of driving style using data-dependent pointer model

Popis výsledku v původním jazyce

The paper focuses on a task of stochastic modeling the driving style and its online estimation while driving. The driving style is modeled by means of a mixture model with normal and categorical components as well as a data-dependent pointer. The main contributions of the presented approach are: (i) the online estimation of the driving style while driving, taking into account data up to the current time instant, (ii) the joint model for continuous and discrete data measured on a vehicle, (iii) the data-dependent model of the driving style conditioned by the values of fuel consumption, (iv) the use of the model both for detection of clusters according to the driving style and prediction of the fuel consumption along with other variables, and (v) the universal modeling with the help of mixtures, which allows us to use different combinations of components and pointer models as well as to specify the initialization approach suitable for the considered problem.

Název v anglickém jazyce

An online estimation of driving style using data-dependent pointer model

Popis výsledku anglicky

The paper focuses on a task of stochastic modeling the driving style and its online estimation while driving. The driving style is modeled by means of a mixture model with normal and categorical components as well as a data-dependent pointer. The main contributions of the presented approach are: (i) the online estimation of the driving style while driving, taking into account data up to the current time instant, (ii) the joint model for continuous and discrete data measured on a vehicle, (iii) the data-dependent model of the driving style conditioned by the values of fuel consumption, (iv) the use of the model both for detection of clusters according to the driving style and prediction of the fuel consumption along with other variables, and (v) the universal modeling with the help of mixtures, which allows us to use different combinations of components and pointer models as well as to specify the initialization approach suitable for the considered problem.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA15-03564S" target="_blank" >GA15-03564S: Shlukování a klasifikace s využitím rekurzivního odhadování modelu směsi distribucí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Transportation Research. Part C: Emerging Technologies

ISSN

0968-090X

e-ISSN

—

Svazek periodika

86

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

23-36

Kód UT WoS článku

000425566000002

EID výsledku v databázi Scopus

2-s2.0-85033590254