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Title:Primerjava različnih načinov učenja globokih nevronskih mrež v avtonomni vožnji : magistrsko delo
Authors:ID Skupek, Andraž (Author)
ID Holobar, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Skupek_Andraz_2022.pdf (2,41 MB)
MD5: 9CE7D4A376FEED0233E9819CFCBD56EF
PID: 20.500.12556/dkum/70b21209-9f1d-4f25-ae13-6fbe0d990861
 
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 opisujemo avtonomno vožnjo, algoritme za učenje avtonomnih vozil ter algoritme za razpoznavo prometnih znakov. Za implementacijo smo uporabili dva različna načina učenja avtonomnih vozil, in sicer posnemajoče učenje – za implementacijo katerega smo uporabili konvolucijske nevronske mreže, ter samoojačitveno učenje, kjer uporabljamo nevronsko mrežo, model pa se uči iz lastnih napak. Ob implementaciji avtonomnih vozil smo s pomočjo konvolucijskih nevronskih mrež implementirali tudi modele za razpoznavo prometnih znakov. Omenjene modele smo nato združili z algoritmi avtonomne vožnje in s tem dobili vozilo, ki se je sposobno v simulatorju samostojno premikati ter pospeševati ali zavirati glede na razpoznani prometni znak. Modele obeh načinov avtonomne vožnje testiramo na osmih različnih progah, kjer hitrost vožnje upravljamo tudi s pomočjo razpoznavalnika prometnih znakov. Modeli so uspešni, če uspešno prevozijo celotno progo. Rezultati naših modelov so uspešni, saj je kar nekaj modelov uspešno premagalo vseh osem prog.
Keywords:Avtonomna vožnja, globoko učenje, nevronske mreže, konvolucijske nevronske mreže
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[A. Skupek]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (X, 75 f.))
PID:20.500.12556/DKUM-81314 New window
UDC:004.85:004.032.26(043.2)
COBISS.SI-ID:103252483 New window
Publication date in DKUM:14.03.2022
Views:1122
Downloads:188
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
SKUPEK, Andraž, 2022, Primerjava različnih  načinov učenja  globokih nevronskih  mrež v avtonomni  vožnji : magistrsko delo [online]. Master’s thesis. Maribor : A. Skupek. [Accessed 28 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=81314
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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:20.02.2022

Secondary language

Language:English
Title:Comparison of different deep neural network learning algorithms in autonomous driving
Abstract:The goal of this thesis was to study and demonstrate the performance of algorithms for autonomous driving and algorithms for traffic signs detection and recognition. We used two approaches to autonomous driving. The first one is called behavioural cloning and is using convolutional neural networks. The second one is reinforcement learning. We also implemented models for traffic signs detection and recognition. Finally, we combined those models with autonomous driving models and we simulated the control of an autonomous car which can accelerate and brake according to a recognized traffic signs. All autonomous driving models were tested on eight different simulated tracks, on which the speed of driving was controlled by traffic sign detection models. The model was marked as successful when it successfully completed the track. As demonstrated by our results, several models successfully completed all eight test tracks.
Keywords:Autonomus driving, neural networks, convolutional neural networks


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