A hybrid approach for adaptive car navigation

Authors:
Siamak Barzegar;Maryam Davoudpour;Alireza Sadeghian
Affiliations:
Department of Computer Engineering, Islamic Azad University of Qazvin, Qazvin, Iran;Department of Computer Science, Ryerson University Toronto, Canada;Department of Computer Science, Ryerson University Toronto, Canada
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

Citing 5
Cited 0

Real-time heuristic search

Artificial Intelligence
Finding shortest paths in large network systems

Proceedings of the 9th ACM international symposium on Advances in geographic information systems
A Fast and Efficient Route Finding Method for Car Navigation Systems with Neural Networks

EDOC '06 Proceedings of the 10th IEEE International Enterprise Distributed Object Computing Conference
Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems

International Journal on Software Tools for Technology Transfer (STTT)
Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks

IEEE Transactions on Intelligent Transportation Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is intended to present a method to find an optimized route with intelligent devices for vehicles. Because the vehicles routing problem is one of the possible applications in which the demands of the driver are not specified, this proposed method will use learning automata and fuzzy logics in dynamic environment in order to learn user behavior to predict future behavior and propose an optimized route for the user. The results show that the proposed route is very close to the user desired one.