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Title:
Uporaba nevronske mreže za krmiljenje simuliranega avtonomnega vozila
Authors:
ID
Borko, Aljaž
(Author)
ID
Strnad, Damjan
(Mentor)
More about this mentor...
Files:
MAG_Borko_Aljaz_2017.pdf
(5,28 MB)
MD5: DD83C239FAAFCE7DB72226A671E675AD
PID:
20.500.12556/dkum/778f58cc-9b52-4b3d-a4e2-db1fe3570b70
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu smo preučili nevronske mreže in njihovo uporabo za učenje vožnje simuliranih avtonomnih vozil. Pripravili smo 3D-okolje s cesto, po kateri so se vozila učila voziti. Vsako vozilo ima svojo nevronsko mrežo, ki določa hitrost in smer vozila. Preučili in primerjali smo različne pristope učenja – evolucijski pristop in nadzorovano učenje z metodo vzvratnega prenosa napake. Cilj magistrskega dela je bil ustvariti simulirano avtonomno vozilo, ki je sposobno pravilne vožnje po desni strani ceste in se zna izmikati oviram med vožnjo.
Keywords:
nevronska mreža
,
evolucijski algoritmi
,
3D-simulacija
,
avtonomna vozila
Place of publishing:
[Maribor
Publisher:
A. Borko
Year of publishing:
2017
PID:
20.500.12556/DKUM-68576
UDC:
004.032.26:004.383.4(043.2)
COBISS.SI-ID:
20971286
NUK URN:
URN:SI:UM:DK:N2OIFIUN
Publication date in DKUM:
26.10.2017
Views:
1482
Downloads:
157
Metadata:
Categories:
KTFMB - FERI
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:
BORKO, Aljaž, 2017,
Uporaba nevronske mreže za krmiljenje simuliranega avtonomnega vozila
[online]. Master’s thesis. Maribor : A. Borko. [Accessed 26 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=68576
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
27.09.2017
Secondary language
Language:
English
Title:
Using a neural network to control a simulated autonomous vehicle
Abstract:
In the present research, we studied neural networks and their level of application in learning how to drive simulated autonomous vehicles. We created a 3D environment including a road on which the vehicles can train how to drive. Each vehicle has its own neural network that determines the direction and speed of the vehicle. We compared and studied different approaches to learning – evolutionary approach and supervised learning using the backpropagation method. The aim of this master thesis was to create a vehicle that is able to drive on the right side of the road and avoid the obstacles.
Keywords:
neural network
,
evolutionary algorithms
,
3D simulation
,
autonomous vehicle
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