Title :
Mixture-based cluster detection in driving-related data
Author :
Nagy, Ivan ; Suzdaleva, Evgenia ; Pecherkova, Pavla ; Urbaniec, Krzysztof
Author_Institution :
Fac. of Transp. Sci., Czech Tech. Univ., Prague, Czech Republic
Abstract :
The paper deals with detection of clusters in data measured on a driven vehicle. Such a clustering aims at distinguishing various driving styles for eco-driving and driver assistance systems. The task is solved with the help of the application of the recursive Bayesian mixture estimation theory. The main contribution of the paper is a demonstration that real measurements with non-linear relationships between them can be approximately described by the mixture model, which is known as the universal approximation. Validation experiments are shown.
Keywords :
Bayes methods; approximation theory; driver information systems; estimation theory; mixture models; pattern clustering; driver assistance system; driving styles; driving-related data; eco-driving system; mixture model; mixture-based cluster detection; nonlinear relationships; recursive Bayesian mixture estimation theory; universal approximation; Approximation algorithms; Clustering algorithms; Estimation; Gears; Pressing; Switches; Vehicles; Bayes methods; Gaussian mixture model; clustering algorithms; data analysis; data models; recursive estimation;
Conference_Titel :
Smart Cities Symposium Prague (SCSP), 2015
Conference_Location :
Prague
DOI :
10.1109/SCSP.2015.7181548
