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Journal of Advanced Transportation
Volume 2018 (2018), Article ID 1758731, 20 pages
https://doi.org/10.1155/2018/1758731
Research Article

Driver and Path Detection through Time-Series Classification

1Giustino Fortunato University, Benevento, Italy
2Unitelma Sapienza, Rome, Italy
3National Research Council of Italy (CNR), Pisa, Italy

Correspondence should be addressed to Marta Cimitile; ti.amletinu@elitimic.atram

Received 7 September 2017; Revised 17 January 2018; Accepted 12 February 2018; Published 22 March 2018

Academic Editor: Aboelmaged Noureldin

Copyright © 2018 Mario Luca Bernardi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Driver identification and path kind identification are becoming very critical topics given the increasing interest of automobile industry to improve driver experience and safety and given the necessity to reduce the global environmental problems. Since in the last years a high number of always more sophisticated and accurate car sensors and monitoring systems are produced, several proposed approaches are based on the analysis of a huge amount of real-time data describing driving experience. In this work, a set of behavioral features extracted by a car monitoring system is proposed to realize driver identification and path kind identification and to evaluate driver’s familiarity with a given vehicle. The proposed feature model is exploited using a time-series classification approach based on a multilayer perceptron (MLP) network to evaluate their effectiveness for the goals listed above. The experiment is done on a real dataset composed of totally 292 observations (each observation consists of a given person driving a given car on a predefined path) and shows that the proposed features have a very good driver and path identification and profiling ability.