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Mixture-based cluster detection in driving-related data

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.