The estimation of user performance analytics in the area of car driver performance was carried out in this paper. The main focus relies on the descriptive analysis with our approaches emphasizing on educational serious games, in order to improvise the driver’s behavior (specifically green driving) in a pleasant and challenging way. We also propose a general Internet of the Things (IoT) social gaming platform (SGP) concept that could be adaptable and deployable to any kind of application domain. The social gaming scenario in this application enables the users to compete with peers based on their physical location. The efficient drivers will be awarded with virtual coins and gained virtual coins can be used in real world applications (such as purchasing travel tickets, reservation of parking lots, etc.). This research work is part of TEAM project co-funded within the EU FP7 ICT research program.

Assessment of Driver Behavior Based on Machine Learning Approaches in a Social Gaming Scenario

DANGE, GAUTAM RAVINDRA;PARANTHAMAN, PRATHEEP KUMAR;SAMARITANI, MARCO;BELLOTTI, FRANCESCO;BERTA, RICCARDO;DE GLORIA, ALESSANDRO
2017

Abstract

The estimation of user performance analytics in the area of car driver performance was carried out in this paper. The main focus relies on the descriptive analysis with our approaches emphasizing on educational serious games, in order to improvise the driver’s behavior (specifically green driving) in a pleasant and challenging way. We also propose a general Internet of the Things (IoT) social gaming platform (SGP) concept that could be adaptable and deployable to any kind of application domain. The social gaming scenario in this application enables the users to compete with peers based on their physical location. The efficient drivers will be awarded with virtual coins and gained virtual coins can be used in real world applications (such as purchasing travel tickets, reservation of parking lots, etc.). This research work is part of TEAM project co-funded within the EU FP7 ICT research program.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/853745
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