Authors :
Ligitha Sathymayuran; Disne Kajanath
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/y3jd5avu
Scribd :
https://tinyurl.com/35bm4wuc
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24FEB1512
Abstract :
Modern self-driving cars heavily rely on visual
inputs to make decisions and it contains resolving
significant computer vision issues. The development of deep
learning has opened up a number of opportunities to
enhance those computer vision issues and hence be able to
enhance performance in autonomous driving applications.
The primary function of vision-guided systems is object
segmentation to comprehend the surroundings. This study
uses deep learning techniques to create an effective model
of the best path to follow an item on a self-driving vehicle.
And helping with improved decision-making to locate the
least expensive routes during navigation.
Modern self-driving cars heavily rely on visual
inputs to make decisions and it contains resolving
significant computer vision issues. The development of deep
learning has opened up a number of opportunities to
enhance those computer vision issues and hence be able to
enhance performance in autonomous driving applications.
The primary function of vision-guided systems is object
segmentation to comprehend the surroundings. This study
uses deep learning techniques to create an effective model
of the best path to follow an item on a self-driving vehicle.
And helping with improved decision-making to locate the
least expensive routes during navigation.
