Smart Autonomous Vehicle - One Proposed Realisation

The solution presented in this paper demonstrates that even a simple three layers deep, fully connected neural network has the capacity to learn relatively complex tasks as lane keeping and steering a model vehicle by just observing the images from the front facing QVGA camera.

Gespeichert in:

Autor*in:	Igor Ciganović [verfasserIn] Aleksandar Pluškoski [verfasserIn] Miloš Jovanović [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	artificial intelligence neural network autonomous vehicle computer vision robotics

Übergeordnetes Werk:	In: Robotica & Management - Editura Eftimie Murgu, 2016, 25(2020), 1, Seite 9-14
Übergeordnetes Werk:	volume:25 ; year:2020 ; number:1 ; pages:9-14

Links:	Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ070053642

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language	English
source	In Robotica & Management 25(2020), 1, Seite 9-14 volume:25 year:2020 number:1 pages:9-14
sourceStr	In Robotica & Management 25(2020), 1, Seite 9-14 volume:25 year:2020 number:1 pages:9-14
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institution	findex.gbv.de
topic_facet	artificial intelligence neural network autonomous vehicle computer vision robotics Technology (General) Industrial engineering. Management engineering Management information systems
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authorswithroles_txt_mv	Igor Ciganović @@aut@@ Aleksandar Pluškoski @@aut@@ Miloš Jovanović @@aut@@
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callnumber-first	T - Technology
author	Igor Ciganović
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title	Smart Autonomous Vehicle - One Proposed Realisation
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title_sort	smart autonomous vehicle - one proposed realisation
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title_auth	Smart Autonomous Vehicle - One Proposed Realisation
abstract	The solution presented in this paper demonstrates that even a simple three layers deep, fully connected neural network has the capacity to learn relatively complex tasks as lane keeping and steering a model vehicle by just observing the images from the front facing QVGA camera.
abstractGer	The solution presented in this paper demonstrates that even a simple three layers deep, fully connected neural network has the capacity to learn relatively complex tasks as lane keeping and steering a model vehicle by just observing the images from the front facing QVGA camera.
abstract_unstemmed	The solution presented in this paper demonstrates that even a simple three layers deep, fully connected neural network has the capacity to learn relatively complex tasks as lane keeping and steering a model vehicle by just observing the images from the front facing QVGA camera.
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