Unmanned vehicle trajectory tracking by neural networks
Joint Authors
Chouraqui, Samirah
Boumediene, Salma
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 6B (31 Dec. 2016), pp.1020-1023, 4 p.
Publisher
Publication Date
2016-12-31
Country of Publication
Jordan
No. of Pages
4
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
This paper, deals with a path planning and intelligent control of an autonomous vehicle which should m^^e safely in its road partially structured.
This road, involves a number of obstacles like donkey, traffic lights and other vehicles.
In this paper, the Neural Networks (NN)-based technique Artificial Neural Network (ANN) is described to solve the motion-planning problem in Unmanned Vehicle (UV) control.
This is accomplished by choosing the appropriate inputs / outputs and by carefully training the ANN.
The network is supplied with distances of the closest obstacles around the vehicle to imitate what a human driver would see.
The output is the acceleration and steering of the vehicle.
The network has been trained with a set of strategic input-output.
The results show the effectiveness of the technique used, the UV drives around avoiding obstacles.
American Psychological Association (APA)
Chouraqui, Samirah& Boumediene, Salma. 2016. Unmanned vehicle trajectory tracking by neural networks. The International Arab Journal of Information Technology،Vol. 13, no. 6B, pp.1020-1023.
https://search.emarefa.net/detail/BIM-655288
Modern Language Association (MLA)
Chouraqui, Samirah& Boumediene, Salma. Unmanned vehicle trajectory tracking by neural networks. The International Arab Journal of Information Technology Vol. 13, no. 6B (2016), pp.1020-1023.
https://search.emarefa.net/detail/BIM-655288
American Medical Association (AMA)
Chouraqui, Samirah& Boumediene, Salma. Unmanned vehicle trajectory tracking by neural networks. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 6B, pp.1020-1023.
https://search.emarefa.net/detail/BIM-655288
Data Type
Journal Articles
Language
English
Notes
Includes appendix.
Record ID
BIM-655288