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Driver and Path Detection through Time-Series Classification

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.

Journal of Advanced Transportation, 2018

Autori esterni: Mario Luca Bernardi (Università degli Studi Giustino Fortunato), Marta Cimitile (Università degli Studi di Roma Unitelma Sapienza)
Autori IIT:

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: 1758731.pdf

Attività: Sicurezza delle infrastrutture critiche