Authors:
Andrea Ortalda
1
;
Abdallah Moujahid
2
;
Manolo Dulva Hina
3
;
Assia Soukane
3
and
Amar Ramdane-Cherif
4
Affiliations:
1
Politecnico di Torino, 24 Corso Duca degli Abruzzi,10129 Turin, Italy, ECE Paris School of Engineering, 37 quai de Grenelle, 75015 Paris and France
;
2
Université Internationale de Casablanca, Casablanca, Morocco, ECE Paris School of Engineering, 37 quai de Grenelle, 75015 Paris and France
;
3
ECE Paris School of Engineering, 37 quai de Grenelle, 75015 Paris and France
;
4
Université de Versailles St-Quentin-en-Yvelines, 10-12 avenue de l’Europe, 78140 Velizy and France
Keyword(s):
Ontology, Formal Specification, Machine Learning, Safe Driving, Smart Vehicle, Cognitive Informatics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Acquisition
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
In an intelligent vehicle (autonomous or semi-autonomous), detection and recognition of road obstacle is very important for it is the failure to recognize an obstacle on time which is the primary reason for road vehicular accidents that very often leads to human fatalities. In the intelligent vehicle of the future, safe driving is a primary consideration. This is accomplished by integrating features what will assist drivers in times of needs, one of which is avoidance of obstacle. In this paper, our knowledge engineering is focused on the detection, classification and avoidance of road obstacles. Ontology and formal specifications are used to describe such mechanism. Different supervised learning algorithms are used to recognize and classify obstacles. The avoidance of obstacles is implemented using reinforcement learning. This work is a contribution to the ongoing research in safe driving, and a specific application of the use of machine learning to prevent road accidents.