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Context-Awareness Mobile Devices for Traffic Incident Prevention

Several techniques have been developed in last years by automotive industry in order to protect drivers and car passengers. These methods, for instance the automatic brake systems and the cruise control, are able to intervene when there is a dangerous situation. With the aim to minimize these risks, in this paper we propose a method able to suggest to the driver the driving style to adopt in order to avoid dangerous situations. Our method is basically a two-level fuzzy systems: the first one is related to the driver under analysis, while the second one is a centralized server with the responsibility to send suggestions to drivers in order to prevent traffic incidents. We carried out a preliminary evaluation to demonstrate the effectiveness of the proposed method: we obtain of percentage variation ranging from 85.48% to 88.99% in the number of traffic incidents between the scenarios we considered using the proposed method and the scenario without the proposed method applied.

1st International Workshop on Context-Awareness for Multi-Device Pervasive and Mobile Computing, in conjunction with IEEE PerCom 2018, Athens, Greece, 2018

Autori esterni: Albina Orlando (Istituto per le Applicazioni del Calcolo "M. Picone", Consiglio Nazionale delle Ricerche), Vittoria Nardone (Dipartimento di Ingegneria, Università degli Studi del Sannio), Antonella Santone (Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise)
Autori IIT:

Tipo: Contributo in atti di convegno
Area di disciplina: Computer Science & Engineering

File: context-awareness.pdf

Attività: Critical Infrastructure Security