Please use this identifier to cite or link to this item:
Title: Computer Vision for Autonomous Driving
Authors: Kanchana, B.
Peiris, R.
Perera, D.
Jayasinghe, D.
Kasthurirathna, D.
Keywords: Autonomous Driving
Deep Learning
Computer vision
Issue Date: 9-Dec-2021
Publisher: 2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT
Abstract: Computer vision in self-driving vehicles can lead to research and development of futuristic vehicles that can mitigate the road accidents and assist in a safer driving environment. By using the self-driving technology, the riders can be roamed to their destinations without using human interaction. But in recent times self-driving vehicle technology is still at the early stage. Mostly in the rushed areas like cities it becomes challenging to deploy such autonomous systems because even a small amount of data can cause a critical accident situation. In Order to increase the autonomous driving conditions computer vision and deep learning-based approaches are tended to be used. Finding the obstacles on the road and analyzing the current traffic flow are mainly focused areas using computer vision-based approaches. As well as many researchers using deep learning-based approaches like convolutional neural networks to enhance the autonomous driving conditions. This research paper focused on the evaluation of computer vision used in self-driving vehicles.
ISSN: 10.1109/ICAC54203.2021.9671099
Appears in Collections:3rd International Conference on Advancements in Computing (ICAC) | 2021
Department of Computer Science and Software Engineering-Scopes
Research Papers - Dept of Computer Science and Software Engineering
Research Papers - IEEE
Research Papers - School of Natural Sciences

Files in This Item:
File Description SizeFormat 
  Until 2050-12-31
1.56 MBAdobe PDFView/Open Request a copy

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.