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Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle
Linköping University, Department of Computer and Information Science, Software and Systems.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utvärdering av Deep Learning-algoritmer för styrning av ett självkörande fordon (Swedish)
Abstract [en]

With self-driving cars on the horizon, vehicle autonomy and its problems is a hot topic. In this study we are using convolutional neural networks to make a robot car avoid obstacles. The robot car has a monocular camera, and our approach is to use the images taken by the camera as input, and then output a steering command. Using this method the car is to avoid any object in front of it.

In order to lower the amount of training data we use models that are pretrained on ImageNet, a large image database containing millions of images. The model are then trained on our own dataset, which contains of images taken directly by the robot car while driving around. The images are then labeled with the steering command used while taking the image. While training we experiment with using different amounts of frozen layers. A frozen layer is a layer that has been pretrained on ImageNet, but are not trained on our dataset.

The Xception, MobileNet and VGG16 architectures are tested and compared to each other.

We find that a lower amount of frozen layer produces better results, and our best model, which used the Xception architecture, achieved 81.19% accuracy on our test set. During a qualitative test the car avoid collisions 78.57% of the time.

Place, publisher, year, edition, pages
2018. , p. 47
Keywords [en]
computer vision, machine learning, autonomous car, self-driving, neural network, convolutional neural network, cnn
Keywords [sv]
datorseende, maskininlärning, neuronnätverk, självkörande
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-153450ISRN: LIU-IDA/LITH-EX-A--18/034--SEOAI: oai:DiVA.org:liu-153450DiVA, id: diva2:1271666
External cooperation
MindRoad AB
Subject / course
Computer Engineering
Presentation
2018-06-19, John von Neumann, Linköping Universitet, Linköping, 13:15 (English)
Supervisors
Examiners
Available from: 2018-12-18 Created: 2018-12-17 Last updated: 2018-12-18Bibliographically approved

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CiteExportLink to record
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