Designing a deep learning network for self-driving vehicles
(2017) In Bachelor's Theses in Mathematical Sciences NUMK01 20171Mathematics (Faculty of Engineering)
- Abstract
- Autonomous vehicles, or as we usually know them by, self-driving cars, are
often presented as the transportation technology of the future. The possibility of a vehicle to drive itself without any human intervention or manual control finally becomes possible.
The common machine learning algorithm that is being used in autonomous
vehicles is the deep learning network. Deep learning is a machine learning
technique inspired by the biological nervous system. Using deep learning
networks for classification tasks which involve curve and line detection of the roads in order to control steering angle of an autonomous vehicle has proven to be increasingly successful.
The main objective of this thesis it to create our own deep learning... (More) - Autonomous vehicles, or as we usually know them by, self-driving cars, are
often presented as the transportation technology of the future. The possibility of a vehicle to drive itself without any human intervention or manual control finally becomes possible.
The common machine learning algorithm that is being used in autonomous
vehicles is the deep learning network. Deep learning is a machine learning
technique inspired by the biological nervous system. Using deep learning
networks for classification tasks which involve curve and line detection of the roads in order to control steering angle of an autonomous vehicle has proven to be increasingly successful.
The main objective of this thesis it to create our own deep learning network
using convolutional neural networks (CNNs) in order to teach the vehicle
to drive without driver. In this thesis the CNN with small convolutional filters has been trained from scratch using our own images and a back-propagation algorithm. The resulting model is tested on a driving simulator in order to evaluate its ability to drive itself. (Less) - Popular Abstract
- Autonomous vehicles, or as we usually know them by, self-driving cars, are
often presented as the transportation technology of the future. The possibility of a vehicle to drive itself without any human intervention or manual control finally becomes possible.
The common machine learning algorithm that is being used in autonomous
vehicles is the deep learning network. Deep learning is a machine learning
technique inspired by the biological nervous system. Using deep learning
networks for classification tasks which involve curve and line detection of the roads in order to control steering angle of an autonomous vehicle has proven to be increasingly successful.
The main objective of this thesis it to create our own deep learning... (More) - Autonomous vehicles, or as we usually know them by, self-driving cars, are
often presented as the transportation technology of the future. The possibility of a vehicle to drive itself without any human intervention or manual control finally becomes possible.
The common machine learning algorithm that is being used in autonomous
vehicles is the deep learning network. Deep learning is a machine learning
technique inspired by the biological nervous system. Using deep learning
networks for classification tasks which involve curve and line detection of the roads in order to control steering angle of an autonomous vehicle has proven to be increasingly successful.
The main objective of this thesis it to create our own deep learning network
using convolutional neural networks (CNNs) in order to teach the vehicle
to drive without driver. In this thesis the CNN with small convolutional filters has been trained from scratch using our own images and a back-propagation algorithm. The resulting model is tested on a driving simulator in order to evaluate its ability to drive itself. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8918959
- author
- Ibrahim, Nora LU
- supervisor
- organization
- course
- NUMK01 20171
- year
- 2017
- type
- M2 - Bachelor Degree
- subject
- keywords
- Deep learning network, self-driving vehicle, autonomous, Neural network
- publication/series
- Bachelor's Theses in Mathematical Sciences
- report number
- LUNFNA-4013-2017
- ISSN
- 1654-6229
- other publication id
- 2017:K10
- language
- English
- id
- 8918959
- date added to LUP
- 2017-06-28 16:12:03
- date last changed
- 2017-06-28 16:12:03
@misc{8918959,
abstract = {{Autonomous vehicles, or as we usually know them by, self-driving cars, are
often presented as the transportation technology of the future. The possibility of a vehicle to drive itself without any human intervention or manual control finally becomes possible.
The common machine learning algorithm that is being used in autonomous
vehicles is the deep learning network. Deep learning is a machine learning
technique inspired by the biological nervous system. Using deep learning
networks for classification tasks which involve curve and line detection of the roads in order to control steering angle of an autonomous vehicle has proven to be increasingly successful.
The main objective of this thesis it to create our own deep learning network
using convolutional neural networks (CNNs) in order to teach the vehicle
to drive without driver. In this thesis the CNN with small convolutional filters has been trained from scratch using our own images and a back-propagation algorithm. The resulting model is tested on a driving simulator in order to evaluate its ability to drive itself.}},
author = {{Ibrahim, Nora}},
issn = {{1654-6229}},
language = {{eng}},
note = {{Student Paper}},
series = {{Bachelor's Theses in Mathematical Sciences}},
title = {{Designing a deep learning network for self-driving vehicles}},
year = {{2017}},
}