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

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

doi:10.1117/12.3001346

9 August 2023 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, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 127820J (2023) https://doi.org/10.1117/12.3001346
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tameem Hezam, Mohammed Al Mahbashi, Hashem Almadwami, and Ali Almadwami "A convolutional neural network approach for optimal steering control of self-driving cars", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 127820J (9 August 2023); https://doi.org/10.1117/12.3001346

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Machine learning

Education and training

Roads

Cameras

Autonomous driving

Convolutional neural networks

Autonomous vehicles

Show All Keywords

Keywords/Phrases

Search In:

Publication Years