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Issue title: CogInfoCom-Supported Approaches, Models and Solutions in Surface Transportation
Guest editors: Peter Baranyi, Attila Borsos, Salvatore Cafiso and Marian Tracz
Article type: Research Article
Authors: Sik, Dávid* | Ekler, Péter | Lengyel, László
Affiliations: Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Budapest, Hungary
Correspondence: [*] Corresponding author: Dávid Sik, Department of Automation and Applied Informatics, Budapest University of Technology and Economics, 1117 Budapest, Magyar tudósok krt. 2. (Bldg. Q.), Hungary. Tel.: +36 1 463 2870; E-mail: [email protected].
Abstract: Vehicles are not just a means of transportation anymore, but they are also moving entities that collect and transmit a great amount of data, captured by sensors from the internals of the vehicle and from its environment as well. These data extended with smartphone sensors can be a stable base for smart car-based infocommunication systems. There are two main interfaces to extract the data from a vehicle: OBD-II (On-board Diagnostics) and CAN bus (Controller Area Network bus). This paper introduces an advanced measurement tool, called ObdCanCompare. The purpose of this tool is to provide an environment to compare these data streams in real time and conclude how and which interface can be used for derivation of driving decisions. It also enables creating business intelligence reports using the SensorHUB-based backend, which is our data collection and service development for sensor networks, smart devices and cloud-based backend environments. Results show that the OBD-II interface is quite accurate compared to CAN bus, in order to demonstrate this we have created the Social Driving application as a socio-cognitive ICT pilot application that displays vehicle data to users from OBD-II and enables to compare usage statistics of different drivers in the network as a socio-cognitive gamification.
Keywords: OBD-II, CAN, SensorHUB, VehicleICT, driving decision, gamification, socio-cognitive ICT
DOI: 10.3233/IDT-170306
Journal: Intelligent Decision Technologies, vol. 11, no. 4, pp. 423-430, 2017
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