Two-layer pointer model of driving style depending on the driving environment

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00507883" target="_blank" >RIV/67985556:_____/19:00507883 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21260/19:00333293
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0191261519301559" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0191261519301559</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.trb.2019.08.009" target="_blank" >10.1016/j.trb.2019.08.009</a>

Result language
angličtina
Original language name
Two-layer pointer model of driving style depending on the driving environment
Original language description
This paper deals with the task of modeling the driving style depending on the driving environment. The model of the driving style is represented as a two-layer mixture of normal components describing data with two pointers: outer and inner. The inner pointer indicates the actual driving environment categorized as “urban”, “rural” and “highway”. The outer pointer through the determined environment estimates the active driving style from a fuel economy point of view as “low consumption”, “middle consumption” and “high consumption”. All of these driving styles are assumed to exist within each driving environment due to the two-layer model. Parameters of the model and the driving style are estimated online, i.e., while driving using a recursive algorithm under the Bayesian methodology. The main contributions of the presented approach are: (i) the driving style recognition within each of urban, rural and highway environments as well as in the case of switching among them. (ii) the two-layer pointer, which allows us to incorporate the information from continuous data into the model. (iii) the potential use of the data-based model for other measurements using corresponding distributions. The approach was tested using real data.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/8A17006" target="_blank" >8A17006: (Ultra)Sound Interfaces and Low Energy iNtegrated SEnsors</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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