Mr. Ali Almadwami Profile

Ali Almadwami

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 9 August 2023 Paper

A convolutional neural network approach for optimal steering control of self-driving cars

Tameem Hezam, Mohammed Al Mahbashi, Hashem Almadwami, Ali Almadwami

Proceedings Volume 12782, 127820J (2023) https://doi.org/10.1117/12.3001346

KEYWORDS: Machine learning, Education and training, Roads, Cameras, Autonomous driving, Convolutional neural networks, Autonomous vehicles, Video, Unmanned vehicles, Visualization

Read Abstract +

The end-to-end learning framework for autonomous cars has attracted considerable interest in recent years from both industry and academia. To control an autonomous car, this approach requires developing a model that links sensory perception, such as picture frames from a camcorder, to learn the optimal and safe driving actions. To develop a completely automated driving, it is possible to use professional driver experience more efficiently by using the end-to-end learning approach rather than spending a significant amount of money on labelling like bounding boxes for things. The conventional self-driving system, which typically consists of detection, positioning, mapping, and route planning manually fails to perform well with dynamic autonomous vehicles. We proposed a Convolutional Neural Network (CNN) framework for learning the autonomous driving to convert front-facing camera data from raw pixels and learn the steering commands. This end-to-end strategy has shown very promising results compared to the traditional self-driving models. The proposed CNN framework learns to intelligently drive in traffic on local roads with or without lane markings, on motorways, and with the least amount of training data from experts. Additionally, it also works smartly in places with obscure visual cues, including parking lots and gravel roadways. The system spontaneously picks up internal demonstrations of the essential processing phases, like identifying convenient road objects, by learning the human steering angle as the training input.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years