Authors:
Manolo Dulva Hina
1
;
Clement Thierry
1
;
Assia Soukane
1
and
Amar Ramdane-Cherif
2
Affiliations:
1
ECE Paris School of Engineering, France
;
2
Université de Versailles St-Quentin-en-Yvelines, France
Keyword(s):
Internet of Things, Ontology, Machine Learning, Driving Context, Smart Vehicle, Cognitive Informatics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
Abstract:
In this paper, a novel approach of managing driving context information in smart transportation is presented. The driving context refers to the ensemble of parameters that make up the contexts of the environment, the vehicle and the driver. To manage this rich information, knowledge representation using ontology is used and through it, such information becomes a source of knowledge. When this context information (i.e. basically a template or model) is instantiated with actual instances of objects, we can describe any kind of driving situation. Furthermore, through ontological knowledge management, we can find the answers related to various queries of the given driving situation. A smart vehicle is equipped with machine learning functionalities that are capable of classifying any driving situation, and accord assistance to the driver or the vehicle or both to avoid accident, when necessary. This work is a contribution to the ongoing research in safe driving, and a specific applicatio
n of using data from the internet of things.
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