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

0481220 - ÚTIA 2019 RIV NL eng J - Journal Article
Suzdaleva, Evgenia - Nagy, Ivan
An online estimation of driving style using data-dependent pointer model.
Transportation Research. Part C: Emerging Technologies. Roč. 86, č. 1 (2018), s. 23-36. ISSN 0968-090X. E-ISSN 1879-2359
R&D Projects: GA ČR GA15-03564S
Institutional support: RVO:67985556
Keywords : driving style * fuel consumption * mixture-based clustering * data-dependent pointer * recursive mixture estimation
OECD category: Statistics and probability
Impact factor: 5.775, year: 2018
http://library.utia.cas.cz/separaty/2017/ZS/suzdaleva-0481220.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0277004