| bibtype |
J -
Journal Article
|
| ARLID |
0481220 |
| utime |
20240111140949.9 |
| mtime |
20171111235959.9 |
| SCOPUS |
85033590254 |
| WOS |
000425566000002 |
| DOI |
10.1016/j.trc.2017.11.001 |
| title
(primary) (eng) |
An online estimation of driving style using data-dependent pointer model |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0255260 |
| ISSN |
0968-090X |
| title
|
Transportation Research. Part C: Emerging Technologies |
| volume_id |
86 |
| volume |
1 (2018) |
| page_num |
23-36 |
| publisher |
|
|
| keyword |
driving style |
| keyword |
fuel consumption |
| keyword |
mixture-based clustering |
| keyword |
data-dependent pointer |
| keyword |
recursive mixture estimation |
| author
(primary) |
| ARLID |
cav_un_auth*0108105 |
| name1 |
Suzdaleva |
| name2 |
Evgenia |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| institution |
UTIA-B |
| full_dept |
Department of Signal Processing |
| country |
RU |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101167 |
| name1 |
Nagy |
| name2 |
Ivan |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| institution |
UTIA-B |
| full_dept |
Department of Signal Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0321440 |
| project_id |
GA15-03564S |
| agency |
GA ČR |
|
| abstract
(eng) |
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. |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2019 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122142807.9 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0277004 |
| mrcbC64 |
1 Department of Signal Processing UTIA-B 10103 STATISTICS & PROBABILITY |
| confidential |
S |
| mrcbC86 |
1 Article Transportation Science Technology |
| mrcbT16-e |
TRANSPORTATIONSCIENCETECHNOLOGY |
| mrcbT16-j |
1.13 |
| mrcbT16-s |
2.611 |
| mrcbT16-B |
81.395 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1* |
| arlyear |
2018 |
| mrcbTft |
\nSoubory v repozitáři: suzdaleva-0481220.pdf |
| mrcbU14 |
85033590254 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000425566000002 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0255260 Transportation Research. Part C: Emerging Technologies 0968-090X 1879-2359 Roč. 86 č. 1 2018 23 36 Elsevier |
|