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Neurala nätverk försjälvkörande fordon: Utforskande av olika tillvägagångssätt
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
2021 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Neural Networks for Autonomous Vehicles : An Exploration of Different Approaches (English)
Abstract [sv]

Artificiella neurala nätverk (ANN) har ett brett tillämpningsområde och blir allt relevantare på flera håll, inte minst för självkörande fordon. För att träna nätverken användsmeta-algoritmer. Nätverken kan styra fordonen med hjälp av olika typer av indata. I detta projekt har vi undersökt två meta-algoritmer: genetisk algoritm (GA) och gradient descent tillsammans med bakåtpropagering (GD & BP). Vi har även undersökt två typer av indata: avståndssensorer och linjedetektering. Vi redogör för teorin bakom de metoder vi har försökt implementera. Vi lyckades inte använda GD & BP för att träna nätverk att köra fordon, men vi redogör för hur vi försökte. I resultatdelen redovisar vi hur det med GA gick att träna ANN som använder avståndssensorer och linjedetektering som indata. Sammanfattningsvis lyckades vi implementera självkörande fordon med två olika typer av indata.

Abstract [en]

Artificial Neural Networks (ANN) have a broad area of application and are growing increasingly relevant, not least in the field of autonomous vehicles. Meta algorithms are used to train networks, which can control a vehicle using several kinds of input data. In this project we have looked at two meta algorithms: genetic algorithm (GA), and gradient descent with backpropagation (GD & BP). We have looked at two types of input to the ANN: distance sensors and line detection. We explain the theory behind the methods we have tried to implement. We did not succeed in using GD & BP to train ANNs to control vehicles, but we describe our attemps. We did however succeeded in using GA to train ANNs using a combination of distance sensors and line detection as input. In summary we managed to train ANNs to control vehicles using two methods of input, and we encountered interesting problems along the way.

Place, publisher, year, edition, pages
2021. , p. 65
Keywords [en]
artificial neural networks, gradient descent, genetic algorithm, backpropagation, unity, self driving, autonomous vehicles, line detection, neural network, neural net
Keywords [sv]
artificiella neurala nätverk, neurala nätverk, självkörande bilar, självkörande fordon, unity, bakåtpropagering, linjedetektering, gradient descent, genetisk algoritm, neurala nät
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
URN: urn:nbn:se:kau:diva-84560OAI: oai:DiVA.org:kau-84560DiVA, id: diva2:1567098
External cooperation
Sogeti
Subject / course
Computer Science
Educational program
Computer Science
Presentation
2021-06-01, 21:20 (Swedish)
Supervisors
Examiners
Available from: 2021-06-22 Created: 2021-06-15 Last updated: 2021-06-22Bibliographically approved

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CiteExportLink to record
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