Authors:
Juliette Brezillon
1
;
Patrick Brezillon
1
;
Thierry Artieres
1
and
Charles Tijus
2
Affiliations:
1
LIP6, University Paris 6, France
;
2
Cognition et Usages, University Paris 8, France
Keyword(s):
Driver modeling, Cognitive Sciences, Machine Learning, Context, Situation Awareness.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Computer-Supported Education
;
Data Engineering
;
e-Learning
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Verification and Validation of Knowledge-Based Systems
;
Web Information Systems and Technologies
Abstract:
Although a driving licence concludes training, such an initial is insufficient because new drivers do not know how to contextualize the learned procedures into effective practices. Our goal is to improve the drivers’ situation awareness, in which the drivers perceive the environment’s events and the projection of their status in a close future. To achieve this goal, we design an educational system for the drivers, which help them to become aware of their driving errors. This educational system aims to identify and correct drivers’ drawbacks. In this paper, we discuss the reasons for associating two approaches: a local approach (resulting from cognitive sciences) and a global approach (resulting from machine learning), and we show the key role that context plays in the driving activity.