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.
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