Abstract

VANET networks belong to a current research field, namely vehicle to vehicle
(V2V) communications and their contribution to road safety. Collision and its
prevention in such networks is a very interesting problem by its human and economic
dimension. In Algeria the number of the road accidents does not cease increasing. The
situation has become worrying. This is explained by the number of victims which
exceeds that listed following fatal diseases or natural disasters. In these conditions, road
safety has become a cross-cutting issue in the context of this thesis, it relates to the body
of knowledge, devices and measures to avoid road accidents. Studies show that most
collisions could be avoided if road users were alerted in time. V2V communication is
seen as a promising solution for intelligent transport systems, it is considered one of the
solutions that allow vehicles to share relevant information i.e. (position, speed,
acceleration, steering angle). Modeling and simulation are used as design tools and
testing for the development of new communications approaches. We proposed a new
communication architecture in a VANET network, the concept of estimating
microscopic parameters (position, speed and direction) is the key of this architecture, it
is based on the kinematic modeling of vehicle type and method of Kalman filter in the
trajectories estimation. Communication messages is established between entities in the
same network : the subject vehicle (SV ) and its neighboring vehicles ( OV) through a
wireless standard called DSRC (Dedicated Short Range Communication) , that is a new
standard dedicated to inter vehicle communication. In this thesis, we are interested at
the design, modeling and at the simulation of a proposed prediction approach – FCAA-
(Forecasting Collision Avoidance Approach), this approach is based on the detection,
prediction and avoidance of the collision. The proposed approach has been validated by
simulating real-world intersections scenarios extracted from the Annaba city. The
results show that the FCAA approach to collision avoidance is enabled with a
significant rate.

Information

Item Type:	Thesis
Divisions:	» Faculty of Science and Technology
ePrint ID:	2870
Date Deposited:	2021-11-24
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/2870

BibTex