Autonomous Vehicle Navigation Using Machine Learning

Abstract

Autonomous vehicles have surfaced as one of the most transformative inventions in ultramodern transportation. A crucial element of their functionality is dependable navigation, particularly the discovery and following of road lanes. This exploration presents a machine literacy- grounded system for lane discovery using OpenCV and Python, with a focus on educational availability and system modularity. A web operation erected with Beaker enables druggies to upload driving vids and view lane discovery labour’s. While this perpetration don't incorporate advanced deep literacy or real- time detector emulsion, it offers a foundational understanding of visual navigation in independent vehicles and sets the stage for unborn development using more sophisticated styles KEYWORDS: Autonomous Vehicles, Machine Learning, Lane Detection, Computer Vision, OpenCV, Python, Flask, Self-Driving Cars, Image Processing, Real-Time Navigation