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Implementation of Deep Convolutional...

Mehdy, A K M Nuhil.

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Implementation of Deep Convolutional Neural Network for Predicting Steering Angle in Autonomous Vehicle Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Implementation of Deep Convolutional Neural Network for Predicting Steering Angle in Autonomous Vehicle Systems./
作者:	Mehdy, A K M Nuhil.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	108 p.
附註:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10619631
ISBN:	9780355369564

Implementation of Deep Convolutional Neural Network for Predicting Steering Angle in Autonomous Vehicle Systems.
Mehdy, A K M Nuhil.

Implementation of Deep Convolutional Neural Network for Predicting Steering Angle in Autonomous Vehicle Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 108 p.

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--Lamar University - Beaumont, 2017.

Autonomous vehicle, also known as driverless, auto or self-driving vehicle is the kind of vehicle that is capable of sensing its environment, taking driving direction along with decision and navigating without human input. Having an advanced control system, an autonomous vehicle use various techniques to identify its surroundings for better navigation through the road. So far, Radar, Lidar, laser light, GPS, Odometry, and Computer Vision are the most important techniques that are being used in different ways by the engineers. Among all these robotic approaches, Computer Vision only can be the most efficient one for describing the actual surroundings of a vehicle; just like us---the human who takes all major functional decisions mostly based on the visual context in front of him.

ISBN: 9780355369564Subjects--Topical Terms:

523869
Computer science.

Implementation of Deep Convolutional Neural Network for Predicting Steering Angle in Autonomous Vehicle Systems.
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500 $a Adviser: Stefan Andrei.
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520 $a Autonomous vehicle, also known as driverless, auto or self-driving vehicle is the kind of vehicle that is capable of sensing its environment, taking driving direction along with decision and navigating without human input. Having an advanced control system, an autonomous vehicle use various techniques to identify its surroundings for better navigation through the road. So far, Radar, Lidar, laser light, GPS, Odometry, and Computer Vision are the most important techniques that are being used in different ways by the engineers. Among all these robotic approaches, Computer Vision only can be the most efficient one for describing the actual surroundings of a vehicle; just like us---the human who takes all major functional decisions mostly based on the visual context in front of him.
520 $a On the other hand, due to the advances in the field of Machine Learning, computational power, and improvement in the field of digital image processing; intelligent computer vision has never been so promising, with new features and capability available. Taking all these into account, a deep learning approach, based on Convolutional Neural Network (CNN) architecture has been revealed in this thesis to take computer vision one step further that can alone help Autonomous Vehicle to both learn how to drive and to do it. A CNN is trained to map raw pixels from a single front-facing camera view directly to steering commands.
520 $a The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. Later while in testing mode, the model can predict a steering angle based on a new input image that it never seen before. Unlike typical machine learning or traditional image processing approach, this model was never been explicitly trained by manually extracted features of image, or to detect outline of roads and lanes. I have designed a novel deep neural network, based on a model proposed by NVidia Corporation in one of their papers that is related to End-To-End Learning. I have also simulated the final model by applying it to an autonomous agent that shows an autonomy value of almost 86%.
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