0507883 20240111141023.220190828235959.9 85070899609 000487311300012 10.1016/j.trb.2019.08.009 16 s. P cav_un_epca*02519840191-26151281 (2019)254-270Elsevier driving style driving environment fuel consumption two-layer pointer recursive mixture estimation mixture-based clustering cav_un_auth*0355927 Suzdaleva Evženie UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing RU Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2019/ZS/suzdaleva-0507883.pdf https://www.sciencedirect.com/science/article/pii/S0191261519301559 cav_un_auth*0351997 8A17006 GA MŠk CZ 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. WOS BB 10000 10100 10103 2020 2 4 A hod sml 4ah 4as 20231122144216.4 RVO:67985556 http://hdl.handle.net/11104/0298856 1 Department of Signal Processing UTIA-B 10103 STATISTICS & PROBABILITY S licence agreement 20190914 Rights & Access 3+4 Article Chemistry Inorganic Nuclear C ENGINEERING.CIVIL|OPERATIONSRESEARCH&MANAGEMENTSCIENCE|ECONOMICS|TRANSPORTATION|TRANSPORTATIONSCIENCE&TECHNOLOGY 5.631 0.96 7.7 0.01741 1.53 12945 183 2.895 49.63 6 3888 Q1 4.005 174 Q1 87.864 91.7 Q1* Q1* 1.89 Q1 95.896 2019 Soubory v repozitáři: suzdaleva-0507883.pdf, suzdaleva-0507883 - licence agreement.pdf 85070899609 SCOPUS PUBMED 000487311300012 WOS pdf cav_un_epca*0251984 Transportation Research. Part B: Methodological 0191-2615 1879-2367 Roč. 128 č. 1 2019 254 270 Elsevier